Evaluation Report Template
Standard format for program evaluation findings
The Prompt
The Prompt
Create a comprehensive evaluation report template for [ORGANIZATION NAME]’s [PROGRAM NAME] covering [REPORT PERIOD] in [GEOGRAPHY].
Quick start (minimal inputs): If you only provide [ORGANIZATION NAME], [PROGRAM NAME], [REPORT PERIOD], [GEOGRAPHY], and [TONE: BALANCED (default), FORMAL, or WARM], generate the full template using defaults for all other fields.
If key details are missing, first ask these 5 questions, then proceed:
1) Primary audience and uses? [e.g., program staff to improve delivery; leadership to inform planning]
2) Funders/board version needed? [Yes/No; which funder/board?]
3) Key demographics for equity breakdowns? [e.g., age, race/ethnicity, income, language, zip code]
4) Main data sources available? [e.g., surveys, interviews, admin data, observations]
5) Team capacity? [TEAM SIZE/RESOURCES: e.g., 3 staff, part-time evaluator, $50K program budget]
DELIVERABLE: Produce a complete, fill-in-ready template document that includes:
- Section headings in the specified order with per-section word ranges
- Writing prompts in [BRACKETS] for users to complete
- At least one short, concrete example in the Executive Summary and one in the Findings section showing the expected level of specificity
- Sample data table shells and visual placeholders with caption prompts
- “In plain language” callouts translating evaluator terms
- “How to adapt for funders/board” notes within each section
- Equity and participation prompts embedded in methods and findings
- Constructive subheads for mixed/negative results: “What we learned / Why this matters / What we’re changing”
LENGTH TARGET: 10–25 pages total (adjustable). If user does not specify a page target, default to 10–25 pages.
TONE: [BALANCED (default) | FORMAL | WARM]
- BALANCED: Professional and approachable
- FORMAL: Grant/board-facing, neutral
- WARM: Community-centered, accessible
AUDIENCES AND USES:
- Primary audience: [PRIMARY AUDIENCE and intended uses]
- Secondary audience(s): [SECONDARY AUDIENCE and intended uses]
- Funders/Board: [FUNDERS/BOARD if applicable]
TEMPLATE STRUCTURE AND SPECIFICATIONS
1) Executive Summary (350–600 words)
Include prompts for:
- Program purpose and who it serves: [Program aims; target population; high-level needs/context]
- Reach and participation: [# served; eligibility; participation intensity/dosage; key demographics]
- Top 2–3 findings: [Quantitative KPI highlights with %/#; qualitative themes with brief quote]
- High-level recommendations: [Most actionable next steps]
- Overall assessment/learning stance: [Balanced summary; what worked; what to improve]
- In plain language: “This section tells busy readers what changed, for whom, and what we’ll do next.”
- How to adapt for funders/board: Lead with outcomes tied to grant objectives; keep to one page; include grant KPIs and cost-per-outcome if available.
Example (model the level of detail and tone):
“In 2024, the Youth Pathways mentoring program served 127 students in Westview (62% Latinx, 28% Black; 71% eligible for free/reduced lunch). Attendance improved for 89% of participants; average chronic absence dropped from 16% to 9% over two semesters. Students completing 10+ mentoring sessions were 2.1x more likely to submit all homework on time. Youth and caregiver interviews highlighted stronger school belonging and improved communication at home. We recommend formalizing caregiver touchpoints and expanding peer mentoring to all cohorts.”
2) Program Overview (300–450 words)
Prompts:
- Goals and theory of change: [Brief aims; core assumptions]
- Target population and eligibility: [Age/grade; geography; barriers addressed]
- Activities and dosage: [What, how often, by whom; training/credentials]
- Staffing/resources: [TEAM SIZE/RESOURCES; partnerships; budget range]
- Context: [Relevant policy/school-year cycles; community factors]
- Logic model reference: [Appendix reference]
- Sample phrasing: “In [YEAR], [PROGRAM] served [#] participants through [KEY ACTIVITIES] to achieve [OUTCOMES].”
- How to adapt for funders/board: Align goals and activities with funded objectives and approved budget.
3) Evaluation Questions & Methodology (300–500 words)
Prompts:
- Evaluation questions (3–5): [e.g., To what extent did participants improve in [OUTCOME]? Which components were most/least helpful and for whom?]
- Design/approach: [Pre-post, cohort tracking, contribution not attribution; any comparison group]
- Data sources: [DATA SOURCES: surveys, interviews, focus groups, admin records, observations; instrument names]
- Sampling and timing: [Who, how many, when; response rates]
- Data quality checks: [Missing data review, reliability checks, spot audits, data cleaning steps]
- Ethics/consent: [Consent process; privacy; IRB status if applicable]
- Equity/participation: [Whose voices shaped questions? Any community reviewers? Accessibility considerations]
- In plain language: “We measured success by [brief description of indicators and how collected].”
- Table shell: Method | Sample/Response Rate | Timeline | Purpose/Question(s) Informed
- How to adapt for funders/board: Keep design description concise, emphasize validity, consent, and alignment with grant indicators.
4) Findings by Outcome Area (400–700 words per outcome)
For each outcome area, include:
- Outcome definition and indicator(s): [Define; list KPIs and targets]
- Quantitative results:
- KPI table shell: Indicator | Target | Actual | % Achieved | Data Source | Notes
- Visual placeholder suggestions:
- Pre/post change → Paired bar or line chart
- Achievement rates → Simple bar chart
- Demographic distribution → Stacked bar chart
- Caption prompts: “Participants showed [X]% improvement in [OUTCOME] from [BASELINE] to [ENDLINE] (n=[N]).”
- Qualitative insights:
- Themes: [2–3 themes with brief evidence]
- Quote placeholder(s): “[Insert 1–2 participant quotes with consent; include ID code]”
- Equity lens:
- Disaggregate by [KEY DEMOGRAPHICS] and interpret gaps
- Prompt: “Whose outcomes improved least? Possible drivers? Actionable responses?”
- Mixed/negative results framing:
- What we learned: [Specific finding]
- Why this matters: [Implication for clients/operations/equity]
- What we’re changing: [Concrete adjustment and when]
- Evidence tags: [Cite source: “Survey Q12,” “Admin attendance record,” “Focus Group 2”]
- How to adapt for funders/board: Lead with grant KPIs; include 1–2 concise visuals; footnote methods; keep quotes brief.
Example (model the expected specificity):
Outcome: Improved on-time homework submission
- Indicator: % of students submitting ≥90% of assignments
- Target: 70%; Actual: 64% (n=114); % Achieved: 91%
- Pre/post: 41% → 64% (+23 points)
- Disaggregation: Grades 9–10: 68%; Grades 11–12: 58%; English learners: 61%; Non-EL: 66%
- Theme: Students credited text reminders and peer study halls. Quote: “The Tuesday study group kept me from falling behind.” — 10th grader (consented)
- What we’re changing: Expand peer study halls to 2x/week for upper grades; pilot multilingual reminders.
5) Discussion/Interpretation (300–500 words)
Prompts:
- Connect findings to program theory: [Which components likely drove change, for whom, and why]
- Triangulate: [How quantitative and qualitative converge/diverge]
- External factors: [School calendar, transportation, policy shifts]
- Surprises: [What was unexpected and how we validated it]
- Learning stance:
- What we learned / Why this matters / What we’re changing
- In plain language: “Here we make sense of why results look the way they do.”
- How to adapt for funders/board: Emphasize accountability, practical lessons, and planned course corrections.
6) Recommendations & Next Steps (250–400 words)
Prompts:
- Tie each recommendation to an insight and an outcome gap
- Make steps specific, time-bound, and feasible given [TEAM SIZE/RESOURCES]
- Table shell:
- Recommendation | Owner/Lead | Timeframe (immediate/3–6 months/12 months) | Resource Needs (staff hours, budget) | Feasibility (H/M/L) | Success Measure
- Sample recommendations:
- “Expand peer mentoring to all cohorts | Program Manager | 6 months | 20 staff hours, $2K | High | ≥75% cohort participation”
- “Standardize caregiver outreach script and schedule | Family Liaison | Immediate | 8 hours setup | High | 80% reach within first month”
- How to adapt for funders/board: Flag any budget-neutral options; identify items requiring reallocation or future grant funding.
7) Limitations (150–250 words)
Prompts:
- Sample size/response rates and representativeness
- Data quality issues: [Missingness, self-report bias]
- Design constraints: [No comparison group; short follow-up window]
- External influences
- Ethics/consent status: [All quotes used with explicit permission; data stored securely]
- In plain language: “Here’s what we couldn’t measure or are less sure about.”
- How to adapt for funders/board: Be transparent and concise; emphasize mitigation steps.
8) Appendices (checklist with placeholders)
- [ ] Logic model or theory of change
- [ ] Data collection instruments (surveys, interview/focus group guides)
- [ ] Detailed data tables (by demographic subgroup)
- [ ] Sample consent forms and data privacy statement
- [ ] Complete indicator list with definitions and calculation notes
- [ ] Additional participant quotes (with consent documentation)
- [ ] Data quality checks documentation (missing data rates; reliability)
Formatting and style requirements
- Define all acronyms on first use
- Use specific numbers and percentages (avoid “many/most”)
- Tie each claim to a data source
- Use simple, plain English; include “In plain language” callouts where technical
- Visuals: include captions, n-sizes, and data sources
- Page target: [PAGE TARGET if specified; otherwise 10–25 pages]
Equity and participation integration
- For each outcome: disaggregate by [KEY DEMOGRAPHICS], interpret gaps, and propose actions
- Prompts: “Whose voices informed this evaluation?” “Who is missing and how will we include them next time?”
- Note access and language accommodations in data collection
- Encourage participatory steps: [participant reviewers; co-interpretation sessions]
Quality standards
Do:
- Use realistic nonprofit examples (e.g., volunteer training completion, client retention, attendance)
- Present successes and challenges honestly
- Make recommendations concrete, resourced, and sequenced
Avoid:
- Hype, defensiveness, or unexplained jargon
- Vague claims or recommendations without owners/timelines
Document details (front matter fields)
- Prepared by: [AUTHOR NAME/TITLE]
- In collaboration with: [EVALUATOR NAME/ORGANIZATION, if external]
- Date: [DATE]
- Report covers: [REPORT PERIOD]
- Geographic scope: [GEOGRAPHY]
- Audiences and uses: [PRIMARY/SECONDARY/FUNDERS-BOARD]
OUTPUT INSTRUCTIONS FOR AI
- Generate a complete, fill-in-ready template document with all sections above, writing prompts in [BRACKETS], table shells, visual placeholders, “In plain language” callouts, equity prompts, and “How to adapt for funders/board” notes within each section.
- Include at least one concise, concrete example paragraph in the Executive Summary and one in the Findings section to demonstrate expected specificity and tone.
- If the user provided only quick-start inputs, apply sensible defaults:
- Tone: BALANCED
- Audiences: Primary—program staff/leadership; Secondary—partners/community; Funders/Board—if specified
- Key demographics: age, race/ethnicity, income, language, geography
- Data sources: surveys, interviews, admin data
- Team capacity: “3-person team; part-time evaluator; $50K program budget”
- End the template with a short “How to refine” note for users, e.g.:
- “Ask: ‘Shorten the methodology to 200 words’”
- “Ask: ‘Add a visual for the retention KPI and write a caption’”
- “Ask: ‘Rewrite for a FORMAL tone for a board packet’”How to Customize
- Replace all [BRACKETED] fields with your specific information
- Adjust tone and length as needed for your audience
- Review and personalize before using
Pro Tips
- Test this prompt with your preferred AI tool before using in production
- Always review AI output for accuracy and appropriateness
- Customize outputs to match your organization’s voice and brand
Related Prompts
(See other prompts in the programs category)
Example Outputs
Compare scenarios: We tested this prompt with 3 different nonprofit contexts. Each scenario shows outputs from GPT-5, Claude, and Gemini. Select a model above each scenario to compare.
Small Community Org
Urban food access nonprofit serving Chicago’s South Side neighborhoods through produce vouchers, peer-led nutrition circles, and corner-store partnerships.
View scenario details
Mid-Size Professional Org
Rural health access nonprofit coordinating telehealth navigation, device lending, and coaching across Appalachian Kentucky.
View scenario details
COMPREHENSIVE EVALUATION REPORT TEMPLATE Mountain Health Access Network (MHAN) — TeleCare Connect Report covers FY2024 (July 1, 2023–June 30, 2024) Geographic scope: Eastern Kentucky (Perry, Knott, Letcher, and Breathitt counties) Page target: 15–20 pages
Front Matter (fill-in fields) - Report title: [Mountain Health Access Network (MHAN) — TeleCare Connect FY2024 Evaluation Report] - Prepared by: Dana Whitaker, Director of Programs - In collaboration with: Bluegrass Evaluation Collaborative, LLC - Date: 2024-08-15 - Program: TeleCare Connect (telehealth readiness, device lending, navigator support) - Report covers: FY2024 (July 1, 2023–June 30, 2024) - Geographic scope: Eastern Kentucky (Perry, Knott, Letcher, and Breathitt counties) - TONE: FORMAL (grant/board-facing, neutral) - Audiences and uses: - Primary audience: [MHAN operations and navigator teams] — [Improve delivery, refine workflows, target outreach] - Secondary audience(s): [Appalachian Regional Healthcare (ARH) clinics and public libraries] — [Coordinate patient access and space/resources] - Funders/Board: KY Cabinet for Health and Family Services; Anthem Medicaid; MHAN Board of Directors — [Accountability to grant objectives, strategic planning] - Contact for questions: [Name, title, email, phone]
In plain language: “This front page tells readers what program we evaluated, when and where it ran, and who the report is for.”
How to adapt for funders/board: Add grant number(s), award period, and an “At-a-Glance” KPI box showing 3–5 grant metrics with targets vs. actuals.
Audiences and Uses (confirm/adjust as needed) - Primary audience and uses: [PRIMARY AUDIENCE: Program staff and leadership] — [Use to improve navigator workflows, prioritize equity gaps, plan 2025 resources] - Secondary audience(s) and uses: [ARH clinics, libraries, community partners] — [Coordinate scheduling, room access, and broadband solutions] - Funders/Board: KY Cabinet for Health and Family Services; Anthem Medicaid; MHAN Board of Directors — [Demonstrate outcomes, cost-effectiveness, and lessons learned]
If key details are missing, ask these five questions, then proceed: 1) Primary audience and uses? [e.g., program staff to improve delivery; leadership to inform planning] 2) Funders/board version needed? [Yes/No; which funder/board?] 3) Key demographics for equity breakdowns? [e.g., age, race/ethnicity, income, language, zip code] 4) Main data sources available? [e.g., surveys, interviews, admin data, observations] 5) Team capacity? [TEAM SIZE/RESOURCES: e.g., 3 staff, part-time evaluator, $50K program budget]
SECTION 1) EXECUTIVE SUMMARY (350–600 words) Purpose - Program purpose and who it serves: Reduce missed appointments and improve chronic disease management by enabling rural residents to access telehealth reliably. - Service area: Eastern Kentucky (Perry, Knott, Letcher, Breathitt). - Timeframe: FY2024 (July 1, 2023–June 30, 2024).
Reach and participation (example values; replace or confirm) - Total enrolled participants: 1,420 enrolled patients (65% Medicaid; 24% age 65+; 2% Spanish-speaking; 6% veterans). - Engagement: Average 3 navigator contacts per participant; 1.8 telehealth visits scheduled; 1.3 completed per participant.
Top findings (headline outcomes) - Completion rate for scheduled telehealth visits rose from 62% to 71% (+9 points; n=3,012 visits). - No-show rate fell from 28% to 16% among enrolled patients (n=1,420). - Patient activation: 57% moved up ≥1 Patient Activation Measure (PAM-10) level (n=612 matched surveys). - Participant voice: “Having someone show me how to click in made all the difference.” — Client C-089 (consented).
High-level recommendations (time-bound; ownership assigned in Section 6) - Add evening navigator hours (Mon–Thu until 8 pm) by Q1 2025. - Integrate bilingual text reminders and American Sign Language (ASL) interpreter scheduling by Q2 2025. - Deploy three additional signal boosters to library private rooms by May 2025.
Overall assessment/learning stance - Navigator coaching, device lending, and secure, private access points improved visit completion and patient activation. Gaps persist among seniors (65+) and patients living >30 miles from clinics due to broadband instability and lower comfort with technology. Addressing language access and first-time user support is essential to close equity gaps.
In plain language: “This section tells busy readers what changed, for whom, and what we’ll do next.”
How to adapt for funders/board: Lead with the three strongest outcome statements tied to grant KPIs. Keep to one page. If available, add cost-per-completed telehealth visit and cost per activation gain.
Concrete example paragraph (model the level of specificity and tone) “In FY2024, TeleCare Connect served 1,420 patients across four Eastern Kentucky counties. Telehealth visit completion increased from 62% to 71% (+9 points; n=3,012 visits), while the no-show rate fell from 28% to 16%. Among 612 patients with matched PAM-10 surveys, 57% moved up at least one activation level (mean level 2.1 to 2.5). Patients attributed success to hands-on device coaching and access to private rooms at partner libraries. However, completion lagged for seniors (68%) and those >30 miles from clinics (66%), largely due to broadband issues. To close these gaps, MHAN will extend evening navigator hours by Q1 2025, implement bilingual reminders and integrated interpreter scheduling by Q2 2025, and install three signal boosters in library rooms by May 2025.”
Prompts for completion - [Summarize purpose and need in 2–3 sentences] - [List total participants and key demographics] - [State 2–3 headline findings with numbers and n-sizes] - [Include 1–2 short quotes with consent status] - [List 3 prioritized recommendations with timelines] - [Brief statement on equity gaps and next steps]
SECTION 2) PROGRAM OVERVIEW (300–450 words) Goals and theory of change - Goal: Increase reliable access to telehealth to reduce missed appointments and improve chronic care outcomes. - Theory of change: Hands-on tech support, reliable connectivity, and patient coaching reduce missed visits and increase readiness for telehealth engagement.
Target population and eligibility - Adults and seniors in remote communities within Perry, Knott, Letcher, and Breathitt counties. - Barriers include connectivity, device access, digital literacy, and transportation backup when telehealth fails.
Core activities and dosage - Navigator pre-visit coaching, test calls, and multichannel reminders before every telehealth appointment. - Device lending (tablets + data plans) up to 90 days. - On-demand technical assistance and printed step-by-step guides. - Library private room reservations and signal booster access. - Staff trained in Health Insurance Portability and Accountability Act (HIPAA) and chronic care protocols.
Staffing/resources - 18 full-time equivalents (FTE): 10 navigators, 2 Community Health Worker (CHW) leads, 1 evaluation manager, 3 admin, 2 IT. - Partnerships: ARH clinics, public libraries, University of Kentucky (UK) Telehealth Lab. - Annual budget: $600,000; device library: 120 tablets.
Context - State policy maintained telehealth parity payments. - Flood recovery and periodic power outages affected connectivity in select months.
Sample phrasing and KPI framing (adapt as needed) “In 2024, TeleCare Connect served 1,420 participants through [‘Telehealth readiness screening,’ ‘Navigator pre-visit coaching and reminders,’ ‘Device lending (tablets + data),’ ‘Library private room reservations,’ ‘Broadband mapping and troubleshooting’] to achieve: - Outcome 1: Completed telehealth visits (proportion of scheduled telehealth visits completed among enrolled patients). - KPI: Telehealth completion rate (%) — Target 75; Actual 71 (95% of target). Data source: EHR (Electronic Health Record) attendance export. Note: Pre 62% → Post 71% (+9 points; n=3,012 visits). - Outcome 2: Reduced no-show rates (scheduled clinic and telehealth visits among enrolled patients). - KPI: No-show rate (%) — Target 18; Actual 16 (112% of target). Data source: EHR scheduling/attendance. - Outcome 3: Patient activation (Patient Activation Measure, PAM-10). - KPI: % moving up ≥1 PAM level — Target 60; Actual 57 (95% of target). Data source: PAM-10 survey (n=612).”
In plain language: “This section explains who we serve, what we do, and the results we aim for.”
How to adapt for funders/board: Map each activity to a funded objective and show target vs. actual for each KPI, citing its data source.
Prompts for completion - [Briefly describe program need and goals] - [Confirm target population and eligibility criteria] - [List activities and typical participant dosage] - [Summarize staffing, partnerships, and budget] - [Contextual factors that influenced operations in FY2024] - [Point to Appendices A–C for logic model, instruments, and data tables]
SECTION 3) EVALUATION QUESTIONS & METHODOLOGY (300–500 words) Evaluation questions (select 3–5) - [To what extent did participants improve in telehealth visit completion?] - [How did no-show rates change among enrolled participants?] - [To what extent did patient activation (PAM-10) improve, and for whom?] - [Which components (coaching, device lending, library rooms) were most/least helpful and for whom?] - [What barriers limited results for seniors, Spanish speakers, and those >30 miles from clinics?]
Design/approach - Approach: Pre–post design with cohort tracking; contribution, not attribution. - Comparison: [If any, describe. If none, explain why.] - Triangulation: Quantitative EHR/PAM-10, navigator logs, and qualitative interviews/focus groups.
Data sources - EHR exports (attendance and no-show data). - Salesforce navigator logs (contacts, coaching, technical issues). - PAM-10 (Patient Activation Measure, 10-item) baseline and follow-up surveys. - Post-visit text/phone survey (satisfaction, barriers). - Key informant interview guide (navigators, library staff, clinic managers).
Sampling and timing - [Describe who was included, how many, and when data were collected; note response rates for PAM-10 and post-visit survey.] - [Example: PAM-10 matched n=612 (43% of eligible), post-visit survey n=[XX], response rate [XX]%.]
Data quality checks - Missing data review and imputation thresholds. - Reliability checks (internal consistency for PAM-10; cross-check navigator logs with EHR timestamps). - Spot audits of 10% of navigator records. - Data cleaning steps documented in Appendix E.
Ethics/consent and privacy - Consent: Verbal consent via phone script; consent logged in Salesforce. - Privacy: HIPAA-compliant storage; Business Associate Agreements (BAA) with partners. - Institutional Review Board (IRB): [Not required/non-human subjects per [X] determination.]
Equity and participation - Patient Advisory Council (8 members) reviewed tools and preliminary charts in July 2024. - Large-print guides; ASL interpreter scheduling as needed; bilingual survey where feasible.
In plain language: “We measured success by tracking completed telehealth visits, no-shows, and activation scores, and by asking patients and staff what helped and what got in the way.”
Table shell (Methods overview) | Method | Sample/Response Rate | Timeline | Purpose/Questions Informed | |-------|----------------------|---------|----------------------------| | EHR attendance export | [n=1,420; 100% roster] | [Quarterly pulls] | Telehealth completion; no-show trends | | Salesforce navigator logs | [n=1,420; 95% complete] | [Ongoing; monthly QA] | Engagement dosage; barriers/supports | | PAM-10 surveys | [n=612 matched; 43% RR] | [Baseline, 60–90 days] | Activation gains; subgroup differences | | Post-visit survey | [n=[XX]; [XX]% RR] | [Within 72 hours] | Satisfaction; immediate barriers | | Key informant interviews | [n=[XX]] | [Q3–Q4 FY24] | Component value; improvement ideas |
How to adapt for funders/board: Keep to one short paragraph emphasizing validity, consent, and alignment with grant indicators. Move full methodological detail to appendices.
SECTION 4) FINDINGS BY OUTCOME AREA (400–700 words per outcome) Instructions: Repeat the structure below for each outcome: - Outcome A: Telehealth visit completion - Outcome B: Reduced no-show rates - Outcome C: Patient activation (PAM-10)
Outcome A: Telehealth Visit Completion Outcome definition and indicator(s) - Definition: Proportion of scheduled telehealth visits completed among enrolled patients. - Indicators: Telehealth completion rate (%); number of completed telehealth visits.
Quantitative results KPI table shell | Indicator | Target | Actual | % Achieved | Data Source | Notes | |----------|--------|--------|-----------|-------------|-------| | Telehealth completion rate (%) | 75 | 71 | 95 | EHR attendance export | Pre 62% → Post 71% (+9 points; n=3,012 visits) | | # completed telehealth visits | 2,200 | 2,120 | 96 | EHR attendance export | Across 1,420 patients |
Visual placeholders - Figure A1: Pre vs. post telehealth completion rate (paired bars). Caption: “Telehealth completion increased from 62% to 71% (+9 points; n=3,012 visits). Source: EHR attendance export.” - Figure A2: Completion rate by distance to clinic (>30 miles vs. ≤30 miles) (bar chart). Caption: “Completion lower for patients >30 miles (66%) vs. ≤30 miles (73%). Source: EHR; disaggregation by geocoded distance.” - Figure A3: Completion rate by age group (<65 vs. 65+) (bar chart). Caption: “Seniors (65+) completed 68% vs. 72% for <65. Source: EHR; age from admin records.”
Qualitative insights - Themes (evidence tags): - Hands-on device coaching reduced login failures (Navigator log audit; Interview: Navigator Lead). - Library private rooms increased privacy and confidence (Focus Group: Library Staff). - Quote placeholders: - “[Having someone show me how to click in made all the difference.] — Client C-089 (consented)” - “[I knew the room was private and the Wi-Fi was strong, so I wasn’t nervous.] — Client [ID], (consented)”
Equity lens - Disaggregate by key demographics (prompts): - Age (65+ vs. <65) — [Insert rates and gap] - Distance to clinic (>30 miles vs. ≤30 miles) — [Insert rates and gap] - Insurance (Medicaid, Medicare, Commercial) — [Insert rates and gap] - Language (Spanish vs. English) — [Insert rates and gap if available] - Interpretation prompts: - Whose outcomes improved least? [e.g., seniors and those >30 miles] - Possible drivers? [Broadband reliability; digital confidence] - Actionable responses? [In-home setup visits for 65+; expand signal boosters in libraries]
Mixed/negative results framing - What we learned: [Completion increased to 71% but remains below 75% target.] - Why this matters: [Incomplete visits reduce timely care for chronic conditions.] - What we’re changing: [Evening navigator hours; mandatory pre-visit test calls for first-time users by Q1 2025.]
Evidence tags - EHR attendance export; Navigator log audit; Interview: Clinic Manager #2; Focus Group: Library Staff.
How to adapt for funders/board: Lead with KPI table and one chart. Keep quotes to one line and footnote data sources. Note percent of target achieved.
Concrete example paragraph (specificity model for an outcome) “Telehealth completion improved from 62% to 71% (+9 points; n=3,012 visits), reaching 95% of the 75% target. Gains were strongest for patients within 30 miles of a clinic (73%), while those farther away completed 66%. Seniors completed 68% vs. 72% for younger adults. Interviews indicate device coaching and library rooms reduced login failures and anxiety, but storm-related outages in February 2024 suppressed completion in remote hollers. To close gaps, MHAN will implement in-home first-session setup for seniors and add three signal boosters in partner libraries.”
Outcome B: Reduced No-Show Rates Outcome definition and indicator(s) - Definition: No-show rate for scheduled clinic and telehealth visits among enrolled patients. - Indicator: No-show rate (%).
Quantitative results KPI table shell | Indicator | Target | Actual | % Achieved | Data Source | Notes | |----------|--------|--------|-----------|-------------|-------| | No-show rate (%) | 18 | 16 | 112 | EHR scheduling/attendance | Pre 28% → Post 16% (−12 points; n=1,420) |
Visual placeholders - Figure B1: No-show rate pre vs. post (line chart). Caption: “No-show rate decreased from 28% to 16% (−12 points; n=1,420). Source: EHR scheduling/attendance.” - Figure B2: No-show by language (bar chart). Caption: “Spanish speakers 22% vs. English speakers 16%. Source: EHR; language from intake.” - Figure B3: No-show by distance to clinic (bar chart). Caption: “>30 miles: 18% vs. ≤30 miles: 15%. Source: EHR.”
Qualitative insights - Themes: - Reminder texts reduced day-of no-shows (Navigator logs). - Backup conversion to phone reduced missed care during broadband outages (Clinic Manager interview). - Quote placeholder: - “[If the video fails, they switch me to a call right away—no more missing the doctor.] — Client C-231 (consented)”
Equity lens - Disaggregate by language, distance, age, insurance. - Focus on underrepresented groups (e.g., Spanish speakers): [Interpret drivers; propose bilingual navigator hire; integrate interpreter scheduling.]
Mixed/negative results framing - What we learned: [Language access gaps persist; Spanish speakers report higher no-shows.] - Why this matters: [Risks widening disparities for limited-English-proficient patients.] - What we’re changing: [Hire bilingual navigator; implement Spanish/English automated reminders by Q2 2025.]
Evidence tags - EHR attendance; Navigator logs; Focus Group: Library Staff.
How to adapt for funders/board: Emphasize that target was exceeded (112% of target) while naming specific equity gap actions.
Outcome C: Patient Activation (PAM-10) Outcome definition and indicator(s) - Definition: Change in Patient Activation Measure (PAM-10) among participants with matched surveys. - Indicators: % moving up ≥1 PAM level; change in mean PAM level.
Quantitative results KPI table shell | Indicator | Target | Actual | % Achieved | Data Source | Notes | |----------|--------|--------|-----------|-------------|-------| | % moving up ≥1 PAM level | 60 | 57 | 95 | PAM-10 survey | Matched n=612 | | Mean PAM level (baseline → endline) | 2.5 | 2.5 | 100 | PAM-10 survey | Baseline 2.1 → Endline 2.5 |
Visual placeholders - Figure C1: Distribution of PAM levels (stacked bars, baseline vs. endline). Caption: “Shift toward higher activation levels among matched participants (n=612). Source: PAM-10.” - Figure C2: Mean activation gain by age and insurance (clustered bars). Caption: “Seniors gained 0.3 levels vs. 0.5 for <65; Medicaid patients gained 0.5 vs. 0.3 for Medicare. Source: PAM-10.”
Qualitative insights - Themes: - Simple checklists increased confidence logging in (Navigator call audit). - Printed instructions helpful for lower digital literacy (Interviews). - Quote placeholder: - “[The step-by-step sheet stayed by my computer. I don’t panic anymore.] — Client C-477 (consented)”
Equity lens - Disaggregate by age, insurance, language, and county. - Interpretation prompts: - Who advanced least? [Seniors; rural far-distance households] - Drivers? [Digital comfort; caregiver support] - Responses? [In-home setup; caregiver coaching; large-print and pictorial guides]
Mixed/negative results framing - What we learned: [Activation gains fell short by 3 percentage points.] - Why this matters: [Lower self-efficacy predicts future missed care and poorer chronic disease control.] - What we’re changing: [Revise navigator script with teach-back method; add caregiver coaching; launch March 2025.]
Evidence tags - PAM-10; Navigator call audit; Interview: Navigator Lead.
How to adapt for funders/board: One stacked bar visual and one KPI table. Add one brief quote with consent noted.
SECTION 5) DISCUSSION/INTERPRETATION (300–500 words) Connect findings to program theory - Pre-visit coaching and simple checklists boosted confidence and reduced login failures; device lending removed access barriers; private rooms increased privacy and reliability.
Triangulate across sources - EHR improvements match navigator logs (fewer login failures, fewer last-minute cancellations). - Interviews and speed tests explain subgroup gaps (broadband instability correlates with lower completion).
External factors - Telehealth parity payments sustained supply of telehealth slots. - Flood recovery and storm-related outages in February 2024 reduced completion in certain hollers; library signal boosters partially mitigated.
Surprises and nuance - Library rooms outperformed home settings for first-time users, supported by speed test logs and timestamped completion data. - Text reminders were most effective when combined with a brief test call for first-time users.
Learning stance - What we learned: [Navigator coaching is essential but insufficient for seniors and far-distance households.] - Why this matters: [Without targeted supports, the program risks reinforcing geographic and age-related disparities.] - What we’re changing: [Extend hours; in-home setups; bilingual communications; more boosters; teach-back coaching.]
In plain language: “Here we explain why the numbers look this way and what outside factors influenced results.”
How to adapt for funders/board: Emphasize accountability, cost-awareness, and concrete course corrections linked to KPI gaps.
Prompts for completion - [Summarize 2–3 key drivers that explain improvements] - [Name 2–3 factors that explain remaining gaps] - [List the top 3 program adjustments and expected impact]
SECTION 6) RECOMMENDATIONS & NEXT STEPS (250–400 words) Guidance - Tie each recommendation to a specific insight and outcome gap. - Be specific, time-bound, and feasible given 18 FTE, 120 tablets, and a $600,000 budget.
Recommendation table shell | Recommendation | Insight/Gap Addressed | Owner/Lead | Timeframe (Immediate/3–6 months/12 months) | Resource Needs (staff hours, budget) | Feasibility (H/M/L) | Success Measure | |----------------|-----------------------|------------|--------------------------------------------|--------------------------------------|---------------------|-----------------| | Add evening navigator hours (Mon–Thu until 8 pm) | After-work availability; first-time support | Navigator Lead | 3–6 months (Q1 2025) | 0.5 FTE shift; $[XX] | High | ≥75% completion for first-time users | | Integrate bilingual reminders + ASL interpreter scheduling | Language gap; accessibility | Evaluation Manager + IT | 3–6 months (Q2 2025) | Vendor fees $[XX]; 40 hrs IT | High | No-show gap Spanish vs. English ≤3 pts | | Deploy 3 signal boosters in library rooms | Rural broadband instability | IT Lead + Library Partners | 6–12 months (by May 2025) | Hardware $[XX]; install 60 hrs | Medium | +4 pts completion in target sites | | In-home first-session setup for 65+ | Senior activation/completion gap | CHW Leads | Immediate pilot (Q4 2024) | 0.2 FTE; mileage $[XX] | High | 65+ completion rises from 68% to 72% | | Revise navigator scripts with teach-back | Activation shortfall | Evaluation Manager | Immediate | 16 hrs training | High | ≥60% advance ≥1 PAM level |
In plain language: “This is our to-do list with owners, timelines, and how we’ll know it worked.”
How to adapt for funders/board: Flag budget-neutral items and those requiring reallocation or new grant funding. Note any procurement or compliance steps.
Prompts for completion - [Add or edit 3–5 priority recommendations] - [Specify owners, timelines, and success measures] - [Identify budget or staffing implications]
SECTION 7) LIMITATIONS (150–250 words) - Sample size/response rates: [PAM-10 matched n=612 (43% of eligible); potential nonresponse bias toward more engaged patients.] - Representativeness: [Spanish speakers underrepresented (2% of sample); rural far-distance households possibly overrepresented among device borrowers.] - Data quality issues: PAM-10 nonresponse among the oldest patients may bias activation upward; conducted sensitivity checks comparing age and insurance strata. - Design constraints: No randomized comparison group; short follow-up window (60–90 days) may miss longer-term effects on chronic disease outcomes. - External influences: Storm-related broadband outages; clinic scheduling changes; parity policy. - Ethics/consent: Verbal consent logged; encrypted server storage; BAAs with partners; IRB not required.
In plain language: “Here’s what we couldn’t measure or are less sure about, and why.”
How to adapt for funders/board: Be concise and transparent; include 1–2 mitigation steps (e.g., planned oversampling for seniors; expanded bilingual outreach).
SECTION 8) APPENDICES (checklist and placeholders) - [ ] Appendix A: Logic model or theory of change - [Insert diagram or bullet logic model: Inputs → Activities → Outputs → Outcomes → Impact] - [ ] Appendix B: Data collection instruments - [PAM-10 survey; Post-visit survey; Interview and focus group guides] - [ ] Appendix C: Detailed data tables (by demographic subgroup) - [Tables by age, language, distance, insurance, county, and zip code] - [ ] Appendix D: Sample consent forms and data privacy statement - [Phone script; storage and access protocols] - [ ] Appendix E: Complete indicator list with definitions and calculation notes - [Indicator formulas; inclusion/exclusion criteria; data pulls schedule] - [ ] Appendix F: Additional participant quotes (with consent documentation) - [Quote bank with IDs and consent status] - [ ] Appendix G: Data quality checks documentation - [Missing data rates; reliability checks; audit outcomes]
CROSS-CUTTING EQUITY AND PARTICIPATION PROMPTS (embed throughout) - Disaggregation: For each outcome, break out by age, race/ethnicity, income (if available), language, insurance, county/zip code, and distance to clinic. - Interpretation: “Whose outcomes improved least? What likely explains this? What will we do next quarter to address it?” - Participation: “Whose voices informed this evaluation?” [Patient Advisory Council (8 members) reviewed preliminary charts in July 2024.] - Access accommodations: Large-print materials; ASL interpreter scheduling; bilingual reminders; library private rooms; device lending with data. - Missing voices: “Who is missing from our data and how will we include them next time?” [Plan oversampling or targeted outreach.]
FORMATTING AND STYLE REQUIREMENTS - Define acronyms on first use: EHR (Electronic Health Record); PAM-10 (Patient Activation Measure, 10-item); HIPAA; IRB; BAA; CHW; FTE. - Use specific numbers and percentages; avoid vague terms. - Tie each claim to a data source (e.g., “Source: EHR attendance export, FY2024”). - Keep language clear and plain; include “In plain language” callouts in each section. - Visuals: Include captions with baseline, endline, n-sizes, and data sources.
TEMPLATE FILL-IN PROMPTS SUMMARY (use within each section) - [Program purpose in 1–2 sentences] - [Participant reach and composition] - [Key outcomes with numbers and data sources] - [Top 2–3 recommendations with timelines] - [Equity gaps identified and responses] - [Methods summary in ≤150 words for board versions] - [Quotes with consent and ID]
OPTIONAL ONE-PAGE DASHBOARD (for board/funders packet) - Telehealth completion: 62% → 71% (+9 points; n=3,012) — 95% of target - No-show rate: 28% → 16% (−12 points; n=1,420) — 112% of target - Activation: 57% moved up ≥1 PAM level (n=612) — 95% of target - Equity notes: Seniors and >30 miles lagging; Spanish speakers higher no-show - Next steps: Evening hours (Q1 2025); bilingual reminders/ASL (Q2 2025); boosters by May 2025
QUICK COMPLETION CHECKLIST (before publishing) - [ ] All outcomes show target vs. actual with % achieved - [ ] All visuals have captions and data sources - [ ] Equity disaggregation and action steps included - [ ] Consent and privacy statements included - [ ] Recommendations have owners, timelines, and success measures - [ ] “In plain language” boxes present in each section - [ ] “How to adapt for funders/board” notes included
HOW TO REFINE (ask the AI to iterate) - Ask: “Shorten the methodology to 200 words.” - Ask: “Add a visual for the completion KPI and write a caption.” - Ask: “Rewrite for a FORMAL tone for a board packet.” - Ask: “Draft a one-page executive summary with cost-per-outcome.” - Ask: “Create a subgroup analysis table by age and distance with key takeaways.”
AI Evaluation Details (Score: 9.2/10)
This is an exceptionally well-crafted evaluation template that demonstrates deep understanding of nonprofit evaluation practice. The formal tone is perfectly calibrated for grant/board audiences, the structure is comprehensive and practical, and the embedded examples provide concrete guidance. Minor weaknesses include some repetition and slightly formulaic phrasing in places, but these don't significantly detract from its utility.
- • Excellent tone calibration—consistently formal, neutral, and grant-appropriate throughout, with professional language that matches funder expectations
- • Outstanding specificity in examples—the telehealth completion example (62% to 71%, n=3,012) and recommendation table with owners/timelines/success measures are exactly the level of detail nonprofits need
- • Exceptional equity integration—disaggregation prompts, 'whose voices' questions, and 'what we're changing' framing are embedded naturally rather than tacked on
- • Highly practical structure—KPI tables with target/actual/% achieved, visual placeholders with caption prompts, and evidence tags make this immediately usable
- • Strong funder/board adaptation notes—each section includes specific guidance on condensing for different audiences, including cost-per-outcome suggestions
- • Comprehensive appendix checklist—covers all essential evaluation documentation from logic models to consent forms to data quality checks
- • Excellent 'in plain language' callouts—these would genuinely help staff translate evaluator jargon for community audiences
- • Realistic resource constraints—the template acknowledges team capacity (18 FTE, $600K budget) and ties recommendations to feasibility
- • Strong mixed/negative results framing—'what we learned/why this matters/what we're changing' structure models constructive accountability
- • Some repetition between sections—the outcome definitions and KPIs appear in both the Program Overview and Findings sections, which could be streamlined
- • Slightly formulaic phrasing in places—phrases like 'In plain language:' and 'How to adapt for funders/board:' are mechanically repeated rather than varied
- • The 'How to Refine' section at the end, while helpful, breaks the fourth wall in a way that might confuse users who expect a pure template
Large Established Org
Established refugee resettlement agency providing workforce development, ESL, and credentialing support across the Seattle metro.
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Pacific Horizons Refugee Services (PHRS) Career Pathways for New Americans Evaluation Report Template (Fill-in-Ready) Report period: January 1–December 31, 2024 Geographic scope: King County, Washington (Seattle, Kent, Tukwila, Burien, Renton) Tone: BALANCED (professional and approachable) Page target: 18–22 pages
Front matter (complete before drafting) - Title: Career Pathways for New Americans — 2024 Evaluation Report - Prepared by: Lina Nguyen, Senior Evaluation Manager - In collaboration with: PHRS Impact & Learning Unit (internal) - Date: 2025-02-05 - Report covers: January 1–December 31, 2024 - Geographic scope: King County, Washington (Seattle, Kent, Tukwila, Burien, Renton) - Audiences and uses: - Primary: [PHRS program leadership and employment specialists — use to improve service delivery, staffing, and scheduling decisions] - Secondary: [Employer partners and community colleges — use to refine cohorts and bridge courses] - Funders/Board: Office of Refugee Resettlement (ORR), Washington Department of Commerce (WA Commerce), Ballmer Group, PHRS Board of Directors — use to assess performance vs. grant KPIs, inform future funding - Acronyms defined on first use: - PHRS: Pacific Horizons Refugee Services - ORR: Office of Refugee Resettlement - WA UI: Washington State Unemployment Insurance (wage file) - ESL: English as a Second Language - KPI: Key Performance Indicator - ETO: Efforts to Outcomes (PHRS administrative database) - SIV: Special Immigrant Visa
Audiences and Uses (state clearly in your own words) - Primary audience and intended uses: [e.g., PHRS program staff/leadership — to improve service delivery and plan 2025 strategy] - Secondary audience(s) and intended uses: [e.g., Employer partners and community colleges — to align cohorts and hiring expectations] - Funders/Board: ORR; Washington Department of Commerce; Ballmer Group; PHRS Board of Directors — [use to assess progress vs. funded objectives and resource use]
1) Executive Summary (350–600 words) Purpose: Summarize what changed, for whom, and what you’ll do next.
Use this fill-in framework (edit as needed): - Program purpose and who it serves: - [Career Pathways for New Americans helps newly arrived refugees, asylees, SIV holders, and humanitarian parolees secure quality employment aligned with their skills through contextualized ESL, credential evaluation, career coaching, and employer partnerships.] - Reach and participation (2024 defaults — revise if needed): - [Clients served: 1,085 total; 54% women; clients from 46 countries.] - [Top languages: Arabic, Dari, Somali, Ukrainian.] - [Eligibility: Refugee/asylee/SIV/humanitarian parole within 5 years of arrival.] - [Average service duration: 14 weeks; average ESL instructional hours: 48; median career coaching sessions: 6.] - Headline outcomes (2024 defaults — revise if needed): - [Job placement within 90 days: 63% (target: 60%). Data source: ETO admin records, employer verification.] - [90-day retention: 72% of placed clients (target: 70%). Data source: employer verification, case notes.] - [Median starting wage: $21.80 (target: $21.00). Data source: employer verification; WA UI file (lagged).] - [Credential equivalency achieved for 214 clients (target: 200). Data source: WES/credential evaluators.] - [Sample client quote (consented): “My mentor helped me translate my nursing experience to U.S. terms.” — Client U-221] - Equity highlights (defaults — tailor with your data): - [Placement gap by gender: women 59% vs. men 67%.] - [Median wage gap: women $20.10 vs. men $23.00.] - [Planned response: childcare stipend pilot, evening ESL expansion, salary negotiation workshops tailored for women.] - High-level recommendations (defaults — confirm or revise): - ['Pilot childcare stipend for job seekers (Q2 2025)'] - ['Increase evening ESL sections by 25% (Q2 2025)'] - ['Add employer cohort in healthcare support roles (Q3 2025)'] - Overall assessment/learning stance: - [Strong placement and retention supported by robust employer partnerships. Persistent wage and placement gaps for women and clients with limited English proficiency; credential evaluation backlogs delay sector-aligned hiring. We will address these with targeted scheduling, financial supports, and credential process improvements.]
In plain language: This section tells busy readers what changed, for whom, and what we’ll do next.
How to adapt for funders/board: Keep to one page; lead with grant KPIs, cost-per-placement/retained job if available; tie results to specific grant objectives.
Example (model the level of detail and tone): “In 2024, the Youth Pathways mentoring program served 127 students in Westview (62% Latinx, 28% Black; 71% eligible for free/reduced lunch). Attendance improved for 89% of participants; average chronic absence dropped from 16% to 9% over two semesters. Students completing 10+ mentoring sessions were 2.1x more likely to submit all homework on time. Youth and caregiver interviews highlighted stronger school belonging and improved communication at home. We recommend formalizing caregiver touchpoints and expanding peer mentoring to all cohorts.”
2) Program Overview (300–450 words) Purpose: Provide context, goals, activities, and resources.
Use this fill-in framework: - Goals and theory of change: - [Career Pathways aims to connect newly arrived adults to quality, skill-aligned employment. If we provide contextualized ESL, targeted coaching, credential evaluation support, and employer cohort hiring opportunities, then participants will overcome language, credential, and network barriers and enter living-wage jobs more quickly.] - Target population and eligibility: - [Adults (18–64) across King County — Seattle, Kent, Tukwila, Burien, Renton. Eligibility: refugee, asylee, SIV, or humanitarian parole, within five years of arrival. Common barriers: limited English, childcare access, credential recognition, limited professional networks, transportation costs.] - Activities and dosage (defaults — edit to match 2024 operations): - [Contextualized ESL and bridge courses: 4–6 hours/week] - [Career coaching and job readiness workshops: biweekly] - [Credential evaluation and licensing navigation] - [Employer cohort hiring events and paid work experiences] - [Mentor matching and retention coaching] - Staffing and resources (defaults): - [65 staff, including certified employment specialists and ESL instructors; 120 volunteer mentors; partnerships with local colleges and 75 employers; 2024 program budget: $3.2M.] - 2024 context: - [Seattle minimum wage increases; tech sector volatility; rising demand in healthcare support roles; large arrivals from Ukraine and Afghanistan.] - Logic model and documentation: - [See Appendix A: Logic Model; Appendix B: Data Collection Instruments; Appendix C: Indicator Definitions.]
Sample phrasing you can adapt: “In 2024, Career Pathways for New Americans served 1,085 participants through: ‘Contextualized ESL and bridge courses’; ‘Career coaching and job readiness workshops’; ‘Credential evaluation and licensing navigation’; ‘Employer cohort hiring and paid work experience’; and ‘Mentor matching and retention coaching’ to achieve job placement within 90 days (target 60%, actual 63), 90-day retention (target 70%, actual 72), median starting wage ($21.00 target, $21.80 actual), and 214 credential equivalencies (target 200).”
How to adapt for funders/board: Map activities to funded objectives and approved budget lines. Name specific employer and education partners tied to grants.
3) Evaluation Questions & Methodology (300–500 words) Purpose: Explain what you asked, how you measured, and how you protected participants.
Evaluation questions (select 3–5 and refine): - [To what extent did participants achieve job placement within 90 days, 90-day retention, and competitive starting wages?] - [Which program components (ESL, credential support, employer cohorts, coaching) were most/least helpful and for whom?] - [Where do inequities appear (e.g., by gender, language level, country of origin, age), and what are plausible drivers?] - [What factors predict higher wage offers and faster sector-aligned placement?] - [How satisfied are employers and participants with program supports?]
Design/approach: - [Cohort tracking with pre-post indicators; contribution (not attribution) lens; no randomized comparison group; limited benchmarking to county labor data where feasible.]
Data sources: - [ETO admin logs; WA UI wage file (lagged); employer satisfaction survey; multilingual intake/exit participant surveys; focus groups with Arabic, Dari, Somali, and Ukrainian interpretation; case notes; credential evaluator reports.]
Sampling and timing: - [Admin data: full sample (N=[insert]); Surveys: [insert N, response rates]; Focus groups: [# groups, # participants], conducted [months]. Employer survey: [N employers], response rate [X%].]
Data quality checks: - [Missing data review; cross-check self-reported wages with employer verification/UI where available; spot audits of 10% of case files; duplicate record checks; coding reliability check (kappa ≥0.70) for qualitative themes.]
Ethics/consent: - [Written consent in 7 languages; privacy statement aligned with ORR; quotes included only with written permission; identifiable information stored on secure servers; IRB not required.]
Equity and participation: - [Client Advisory Council (12 members) co-designed 6 survey items; interpreters and onsite childcare provided during focus groups; two co-interpretation workshops held with staff and clients (December 2024).]
In plain language: We measured success by tracking placements, retention, wages, and credential outcomes in our database, alongside surveys, interviews, and employer feedback, with participants’ consent and interpreters as needed.
Table shell: Methods overview - Method | Sample/Response Rate | Timeline | Purpose/Questions Informed | Data Quality Steps - [Admin ETO data] | [N=?, 100%] | [Jan–Dec 2024] | [All KPIs; subgroup equity] | [Missing data checks; audits] - [Participant surveys] | [N=?, ?%] | [Intake, exit, 90 days] | [Satisfaction; barriers; helpful components] | [Translation checks; reliability] - [Employer survey] | [N=?, ?%] | [Q3–Q4] | [Readiness; retention drivers] | [Follow-up for nonresponse] - [Focus groups] | [# groups/# participants] | [Q2–Q4] | [Context for gaps] | [Bilingual facilitation; consent]
How to adapt for funders/board: Keep design concise; emphasize alignment to grant indicators, consent procedures, and response rates.
4) Findings by Outcome Area (400–700 words per outcome) Instructions for each outcome: - Define outcome and indicators - Present quantitative results with KPI table and a simple visual placeholder - Add qualitative insights (themes + 1–2 quotes with consent) - Apply an equity lens (disaggregate by key demographics and interpret gaps) - Frame mixed/negative results using: What we learned / Why this matters / What we’re changing - Tag evidence sources (e.g., “ETO admin record,” “Employer survey Q4”)
Key demographics for equity breakdowns (defaults — adapt to your context): - [Age group], [Gender], [Race/ethnicity], [Primary language], [English proficiency level], [Income bracket], [City/ZIP], [Arrival cohort/nationality]
4.1 Outcome A: Job placement within 90 days - Definition and indicator(s): - [Indicator: % of enrolled job-seeking clients placed within 90 days of intake. Target: 60%.] - Quantitative results (defaults — replace with final numbers as needed): - [Actual: 63% placed within 90 days (n=1,085). Source: ETO admin data; employer verification.] - KPI table shell: - Indicator | Target | Actual | % Achieved | Data Source | Notes - % placed within 90 days | 60% | 63% | 105% | ETO; employer verification | [Include unsubsidized and subsidized placements] - Visual placeholder: - [Figure A1 placeholder: Bar chart — Placement rate target vs. actual] - Caption prompt: “Participants achieved [63%] placement within 90 days vs. [60%] target (n=1,085). Source: [ETO; employer verification].” - Disaggregation prompts (enter your data): - [By gender: Women [59%], Men [67%]] - [By English proficiency: Limited [58%], Advanced [69%]] - [By nationality (examples): Ukrainian [65%], Afghan [60%], Somali [61%]] - Interpretation: [Briefly interpret gaps and likely drivers.] - Qualitative insights (themes): - [Employer cohort events accelerated hiring decisions, especially in healthcare support roles.] - [Mentorship improved resume translation and interview confidence.] - Quote placeholder(s): “[‘My mentor helped me translate my nursing experience to U.S. terms.’ — U-221, consented]” - Mixed/negative results: - What we learned: [Limited English proficiency slowed sector-aligned placement for healthcare and IT.] - Why this matters: [Delays can push clients into lower-wage jobs below skill level.] - What we’re changing: [Add evening ESL sections and a sector vocabulary module by Q2 2025; pilot childcare stipends.] - Evidence tags: [ETO admin record; Employer survey Q2; Focus Group B — ESL Cohort] - How to adapt for funders/board: Open with the KPI table and one chart; add a one-sentence equity note; footnote data sources.
Example (expected specificity): - Indicator: % placed within 90 days - Target: 60%; Actual: 63% (n=1,085); % Achieved: 105% - Disaggregation: Women 59%, Men 67%; Limited English 58%, Advanced 69% - Theme: “Cohort hiring events at two hospital systems produced 118 hires in Q3–Q4; candidates averaged 2.3 weeks faster placement than non-cohort seekers.”
4.2 Outcome B: 90-day job retention - Definition and indicator(s): - [Indicator: % of placed clients retained at 90 days post-start. Target: 70%.] - Quantitative results (defaults — replace with final numbers as needed): - [Actual: 72% (n=678 placed clients). Source: employer verification; case notes.] - KPI table shell: - Indicator | Target | Actual | % Achieved | Data Source | Notes - % retained at 90 days | 70% | 72% | 103% | Employer verification; case notes | [Include unsubsidized and subsidized] - Visual placeholder: - [Figure B1 placeholder: Simple bar chart — Retention rates by industry] - Caption prompt: “90-day retention varied by industry: [Healthcare support 76%], [Warehouse/logistics 68%], [IT support 81%], [Hospitality 70%] (n=678). Source: [Employer verification].” - Disaggregation prompts: - [By industry: insert rates] - [By shift type: day vs. swing vs. night] - [By city/ZIP: e.g., Tukwila vs. Seattle] - Interpretation: [Warehouse/logistics retention lagged, suggesting mismatch in schedules and physical demands.] - Qualitative insights: - [Supervisor check-ins and mentor support reduced early churn; transportation subsidies aided punctuality in first month.] - Quote placeholder: “[‘Weekly check-ins helped me solve small issues before they got big.’ — R-310, consented]” - Mixed/negative results: - What we learned: [Retention lowest in warehouse/logistics; caregivers cited childcare conflicts with swing shifts.] - Why this matters: [Early attrition disrupts household income and employer trust.] - What we’re changing: [Extend retention coaching to 120 days for warehouse placements (Q3 2025); co-create job previews with employer partners; advocate for shift flexibility.] - Evidence tags: [Employer verification; Case notes; Mentor survey Q12]
4.3 Outcome C: Starting wages and wage equity - Definition and indicator(s): - [Indicators: Median starting wage (USD); % at/above living wage benchmark; wage gaps by subgroup. Target median: $21.00.] - Quantitative results (defaults — replace as needed): - [Actual median starting wage: $21.80 (n=[insert]); Source: employer verification; WA UI (lagged).] - KPI table shell: - Indicator | Target | Actual | % Achieved | Data Source | Notes - Median starting wage (USD) | $21.00 | $21.80 | 104% | Employer verification; WA UI | [County minimum wage context] - % at/above living wage | [insert] | [insert] | [insert] | [Specify source] | [Define benchmark] - Visual placeholder: - [Figure C1 placeholder: Box-and-whisker — starting wages by gender] - Caption prompt: “Median starting wage by gender (n=[N]): Women [$20.10], Men [$23.00]. Source: [WA UI; employer payroll].” - Disaggregation prompts: - [By gender; English proficiency; city; industry; nationality/arrival cohort] - Interpretation: [Gender wage gap persists ($2.90). Negotiation coaching and childcare appear linked to offers.] - Qualitative insights: - [Credential recognition correlated with higher offers in healthcare; negotiation coaching boosted IT support offers.] - Quote placeholder: “[‘Once my degree was recognized, employers took my application seriously.’ — D-144, consented]” - Mixed/negative results: - What we learned: [Credential evaluation timelines delay higher-wage, sector-aligned placements.] - Why this matters: [Delays push clients toward short-term, lower-wage jobs.] - What we’re changing: [Batch WES submissions monthly; prescreen credential pathways during intake; offer bridge roles with advancement ladders.] - Evidence tags: [WA UI wage file; WES outcomes; Employer survey Q6]
4.4 Outcome D: Credential recognition and utilization - Definition and indicator(s): - [Indicators: # credential equivalencies or license milestones; % using credential in job placement; time-to-equivalency.] - Targets (defaults): [200 credential equivalencies; achieved 214.] - Quantitative results: - [Actual: 214 credentials recognized (n applicants=[insert]); median time-to-equivalency: [insert weeks]. Source: WES/board records; ETO.] - KPI table shell: - Indicator | Target | Actual | % Achieved | Data Source | Notes - # credential equivalencies | 200 | 214 | 107% | WES; licensing boards | [Top fields: nursing, engineering, teaching] - % placed using credential | [insert] | [insert] | [insert] | ETO; employer verification | [Define “using credential”] - Visual placeholder: - [Figure D1 placeholder: Funnel — applicants → submitted → evaluated → recognized → sector-aligned job] - Caption prompt: “Credential pipeline outcomes, 2024 (n=[N]). Source: [WES; licensing boards; ETO].” - Disaggregation prompts: - [By field (health, education, engineering); by gender; by language; by country.] - Interpretation: [Backlogs highest in [field]; women report more interruptions due to caregiving.] - Qualitative insights: - [Clients credit one-on-one navigation for overcoming documentation hurdles; employers value clear credential summaries.] - Quote placeholder: “[‘The step-by-step guide helped me avoid mistakes and saved time.’ — A-078, consented]” - Mixed/negative results: - What we learned: [Applicants with incomplete documentation face multi-month delays.] - Why this matters: [Extended timelines reduce earnings and talent utilization in shortage fields.] - What we’re changing: [Add document checklists at intake; schedule monthly credential clinics with interpreters; fund application fees for priority sectors.] - Evidence tags: [Credential tracking log; Client interviews; Case notes]
How to adapt for funders/board (applies to all four outcomes): Lead with KPI tables and one visual per outcome; keep quotes short; add footnotes for methods and any data caveats.
5) Discussion/Interpretation (300–500 words) Purpose: Make sense of results and connect back to your theory.
Prompts: - Connect findings to theory of change: - [Employer cohort hiring plus mentor support accelerated placements; credential recognition combined with sector ESL improved wage offers in healthcare support.] - Triangulate evidence: - [ETO/admin trends align with employer survey feedback on readiness; participant interviews identify childcare and language as persistent barriers that explain gender and proficiency gaps.] - External factors: - [Higher minimum wage and improved transit access aided outcomes; tech sector layoffs shifted some clients into IT support pathways with strong retention.] - Surprises: - [Faster-than-expected wage gains in healthcare support; verified through employer wage surveys and WA UI files.] - Learning stance (use the framing): - What we learned: [e.g., Providing more evening ESL improved attendance among caregivers.] - Why this matters: [Increases equitable access and reduces placement delays.] - What we’re changing: [Scale evening sections; embed childcare supports and negotiation workshops.]
In plain language: Here we explain why outcomes look the way they do and what that means for clients and operations.
How to adapt for funders/board: Emphasize accountability, lessons tied to KPIs, and clear course corrections with timelines.
6) Recommendations & Next Steps (250–400 words) Purpose: Translate insights into specific, resourced actions.
Guidance: - Tie each recommendation to an insight, outcome, and equity gap. - Ensure feasibility with available resources: 65 staff across workforce and ESL, 3 analysts in the Evaluation Unit, 120 mentors, $3.2M budget. - Sequence actions (immediate, 3–6 months, 12 months).
Table shell: - Recommendation | Owner/Lead | Timeframe (immediate/3–6 months/12 months) | Resource Needs (staff hours, budget) | Feasibility (H/M/L) | Success Measure - Expand evening ESL by 25% in Seattle/Kent | [ESL Director] | 3–6 months | [80 staff hours; $15K adjuncts] | [H] | [Attendance +15%; placement gap narrows by 3 pts] - Pilot childcare stipend for job seekers | [Program Manager] | 3–6 months | [$30K pilot fund] | [M] | [Placement for women +4 pts; retention +3 pts] - Add healthcare support employer cohort | [Employer Relations Lead] | 6–12 months | [40 hrs; partner MOUs] | [H] | [100 cohort hires; 90-day retention ≥75%] - Batch WES submissions monthly + intake screening | [Credential Navigator] | Immediate | [20 hrs setup] | [H] | [Time-to-equivalency -3 weeks] - Salary negotiation workshops for women | [Career Coach Lead] | Immediate | [10 hrs/month; volunteer mentors] | [H] | [Reduce wage gap by $0.75]
Sample recommendations (you can adapt): - “Expand peer mentoring to all cohorts | Program Manager | 6 months | 20 staff hours, $2K | High | ≥75% cohort participation” - “Standardize caregiver outreach script and schedule | Family Liaison | Immediate | 8 hours setup | High | 80% reach within first month”
How to adapt for funders/board: Flag budget-neutral options; identify items requiring reallocation or future grant funding; link to grant deliverables.
7) Limitations (150–250 words) Purpose: Be transparent about uncertainty and constraints.
Prompts (defaults — revise with your specifics): - Sample/response: [Employer survey response rate was [X%]; participant exit survey [Y%], potentially biasing satisfaction upward.] - Missing data: [WA UI wage data missing for ~29% of placed clients due to recency or SSN gaps; cross-checked with employer payroll when available.] - Design constraints: [No comparison group; short follow-up window (90 days) may miss late attrition or delayed wage growth.] - External influences: [Sector shifts and policy changes (minimum wage) confound causal interpretation.] - Ethics/consent: [Documented multi-language consents; de-identified analysis; secure servers.] - Mitigations: [Triangulated multiple sources; sensitivity checks; subgroup analyses.]
In plain language: Here’s what we couldn’t measure or are less sure about, and how we tried to reduce those gaps.
How to adapt for funders/board: Keep concise; clearly state steps taken to mitigate limitations and any impact on interpreting KPIs.
8) Appendices (checklist with placeholders) Attach or link as available: - [ ] Appendix A: Logic model or theory of change [Insert diagram or one-page logic model] - [ ] Appendix B: Data collection instruments [Surveys; interview/focus group guides; employer survey] - [ ] Appendix C: Detailed data tables by demographic subgroup [CSV or spreadsheet] - [ ] Appendix D: Sample consent forms and data privacy statement [Multi-language] - [ ] Appendix E: Complete indicator list with definitions and calculation notes - [ ] Appendix F: Additional participant quotes (with consent documentation) - [ ] Appendix G: Data quality checks (missing data rates; audit logs; reliability statistics)
Visuals and Tables: Placeholders and Captions - Place each visual with: - Title [Insert descriptive title] - Caption [What the reader should see in one sentence]; include n-size and data source - Notes [Any caveats or definitions] - Sample visual placeholders you can copy: - [Figure X: Placement within 90 days by gender — Bar chart] - Caption: “Women [59%] vs. men [67%] placement rate (n=[N]). Source: [ETO; employer verification].” - [Figure Y: Starting wages by industry — Box plot] - Caption: “Median wages ranged from [$19.80] in hospitality to [$24.10] in IT support (n=[N]). Source: [WA UI].” - [Figure Z: Credential pipeline — Funnel] - Caption: “Of [N] credential applicants, [N1] submitted complete packages, [N2] recognized, [N3] placed in sector roles. Source: [WES; ETO].”
Equity and Participation Prompts (embed throughout) - Disaggregate by: [Age; Gender; Race/Ethnicity; Primary Language; English proficiency; Income; City/ZIP; Arrival cohort/nationality]. - Interpretation prompts: - “Whose outcomes improved least? What are the likely drivers (e.g., scheduling, childcare, transport, language level, discrimination)?” - “What operational changes will we test to close these gaps?” - Participation: - “Whose voices informed this evaluation? Who is missing, and how will we include them next time?” - “Note access supports in data collection (interpreters, childcare, transportation vouchers).” - Participatory steps (defaults): - [Two co-interpretation workshops with staff and Client Advisory Council (December 2024).]
Formatting and Style Requirements (apply consistently) - Define all acronyms on first use. - Use specific numbers and percentages; avoid “many/most.” - Tie each claim to a data source. - Use plain English; add “In plain language” callouts for technical terms. - Visuals: include captions, n-sizes, and data sources. - Include constructive subheads for mixed/negative results: “What we learned / Why this matters / What we’re changing.”
Data Tables: Reusable Shells 1) KPI summary table (all outcomes) - Outcome/Indicator | Target | Actual | % Achieved | N | Data Source | Notes - Placement within 90 days | 60% | 63% | 105% | 1,085 | ETO; employer verification | Includes subsidized/unsubsidized - 90-day retention | 70% | 72% | 103% | 678 | Employer verification; case notes | — - Median starting wage (USD) | $21.00 | $21.80 | 104% | [N] | Employer verification; WA UI | County minimum wage context - Credential equivalencies (#) | 200 | 214 | 107% | [N] | WES; licensing boards | Top fields: nursing, engineering, teaching
2) Disaggregation table shell - Subgroup | Indicator | Target | Actual | Gap vs. overall | N | Interpretation | Action - Women | Placement | 60% | 59% | -4 pts | [N] | [Childcare/scheduling cited] | [Childcare stipend; evening ESL] - Men | Placement | 60% | 67% | +4 pts | [N] | [—] | [—] - Limited English | Placement | 60% | 58% | -5 pts | [N] | [Language barriers] | [Sector vocabulary modules]
3) Methods overview table (replicate from Section 3) - Method | Sample/Response Rate | Timeline | Purpose/Questions Informed | Data Quality Steps - [Fill as above]
“In plain language” callouts (examples you can reuse) - Placement within 90 days: “How quickly people got a job after joining.” - 90-day retention: “Whether they stayed in that job for at least three months.” - Credential equivalency: “Getting overseas education recognized in the U.S.” - Living wage: “Enough pay to meet basic expenses in King County.”
“How to adapt for funders/board” notes (apply per section) - Executive Summary: One page; KPIs first; add cost-per-outcome if available. - Program Overview: Map to grant objectives and budget. - Methods: Emphasize data validity, consent, response rates. - Findings: KPI table + one visual; short, source-cited quotes; footnotes for caveats. - Discussion: Lessons tied to outcomes; realistic next steps. - Recommendations: Time-bound, resourced; flag budget-neutral vs. funding-required. - Limitations: Transparent, concise; note mitigation.
Quick-start defaults used in this template (revise if you have better data) - Reach and participation: 1,085 clients; 54% women; 46 countries; Arabic/Dari/Somali/Ukrainian top languages; average 14 weeks; 48 ESL hours; median 6 coaching sessions. - KPIs: 63% placement within 90 days; 72% 90-day retention; $21.80 median starting wage; 214 credential equivalencies. - Equity patterns: Lower placement and wages for women and limited English; response: evening ESL; childcare stipends; negotiation workshops. - Data sources: ETO admin, WA UI, employer survey, participant surveys and focus groups. - Team capacity: Program staff 65; Evaluation Unit 3 analysts; mentors 120; budget $3.2M.
Optional funder/board adaptation checklist - [ ] Insert grant-specific KPI targets and actuals - [ ] Add cost-per-placement and cost-per-retained job - [ ] Include compliance notes (e.g., ORR reporting fields) - [ ] Add short budget variance note if relevant - [ ] Limit quotes to 1–2 per outcome; keep concise
Endnotes/Footnotes (optional) - [1] WA UI (Washington State Unemployment Insurance) data are lagged by ~1–2 quarters. - [2] All quotes included with written consent; IDs de-identified.
How to refine this template (ask the AI for help) - “Shorten the methodology to 200 words.” - “Add a visual for the retention KPI and write a caption.” - “Rewrite for a FORMAL tone for a board packet.” - “Draft two equity-focused recommendations with owners and timelines.” - “Create a one-page KPI dashboard summary for funders.”
AI Evaluation Details (Score: 8.8/10)
This is an exceptionally thorough, well-structured template that demonstrates deep understanding of nonprofit evaluation practice. It successfully balances technical rigor with accessibility, provides concrete examples at the right level of specificity, and embeds equity considerations throughout. The template is immediately usable and would save practitioners significant time.
- • Outstanding structural completeness—every promised element is present: section headings with word counts, bracketed fill-in prompts, concrete examples in Executive Summary and Findings, table shells, visual placeholders with caption prompts, 'In plain language' callouts, funder/board adaptation notes, and equity prompts
- • Exemplary use of realistic, sector-appropriate examples (e.g., the Youth Pathways example, the credential pipeline funnel, specific wage gaps by gender) that model the expected level of detail without being prescriptive
- • Sophisticated equity integration—disaggregation prompts are embedded in every outcome section with the 'What we learned/Why this matters/What we're changing' framework for addressing gaps, not just reporting them
- • Excellent balance of professional rigor and accessibility—technical terms are defined, plain-language callouts are genuinely helpful, and the BALANCED tone is consistently maintained
- • Highly practical recommendations section with a detailed table shell including Owner, Timeframe, Resource Needs, Feasibility, and Success Measures—this operationalizes insights effectively
- • Thoughtful adaptation guidance for multiple audiences (funders/board notes in every section) without creating separate templates
- • Strong attention to ethical considerations—consent, privacy, language access, and participatory elements are woven throughout, not siloed
- • Comprehensive appendices checklist and reusable table shells that would genuinely accelerate report production
- • The 'How to refine' section at the end provides clear next-step prompts for users to customize further
- • Minor repetition—some guidance (e.g., 'define acronyms,' 'cite sources') appears in multiple sections when a single reference to formatting standards might suffice
- • The template is very long (likely exceeds 22 pages even unfilled), which may be overwhelming for smaller organizations despite the quality; a one-page 'quick navigation guide' at the start could help
- • A few bracketed prompts could be more specific—for example, in the Discussion section, 'Connect findings to theory of change' could include a sentence starter like '[Component X] likely drove [Outcome Y] because [mechanism]...'
- • The authenticity score reflects minor instances of AI-typical phrasing (e.g., 'Here we make sense of why results look the way they do') that, while clear, sound slightly formulaic compared to how an experienced evaluator might write
Test Summary: Generated Nov 2, 2025 • 3 scenarios • 9 total outputs • Average quality score: 9.06/10 • Total validation cost: $0.7896