New insights about community benefit evaluation: Using the Community Health Implementation Evaluation Framework to assess what hospitals are measuring
Bibliographic record
Abstract
BACKGROUND: Nonprofit hospitals are required to conduct community health needs assessments (CHNA) every 3 years and develop corresponding implementation plans. Implemented strategies must address the identified community needs and be evaluated for impact. PURPOSE: Using the Community Health Implementation Evaluation Framework (CHIEF), we assessed whether and how nonprofit hospitals are evaluating the impact of their CHNA-informed community benefit initiatives. METHODOLOGY: We conducted a content analysis of 83 hospital CHNAs that reported evaluation outcomes drawn from a previously identified 20% random sample ( n = 613) of nonprofit hospitals in the United States. Through qualitative review guided by the CHIEF, we identified and categorized the most common evaluation outcomes reported. RESULTS: A total of 485 strategies were identified from the 83 hospitals' CHNAs. Evaluated strategies most frequently targeted behavioral health ( n = 124, 26%), access ( n = 83, 17%), and obesity/nutrition/inactivity ( n = 68, 14%). The most common type of evaluation outcomes reported by CHIEF category included system utilization ( n = 342, 71%), system implementation ( n = 170, 35%), project management ( n = 164, 34%), and social outcomes ( n = 163, 34%). PRACTICE IMPLICATIONS: CHNA evaluation strategies focus on utilization (the number of individuals served), with few focusing on social or health outcomes. This represents a missed opportunity to (a) assess the social and health impacts across individual strategies and (b) provide insight that can be used to inform the allocation of limited resources to maximize the impact of community benefit strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.415 | 0.407 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".