Operationalizing the reach, effectiveness, adoption, implementation, maintenance (RE-AIM) framework to evaluate the collective impact of autonomous community programs that promote health and well-being
Bibliographic record
Abstract
BACKGROUND: The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework is a useful tool for evaluating the impact of programs in community settings. RE-AIM has been applied to evaluate individual programs but seldom used to evaluate the collective impact of community-based, public health programming developed and delivered by multiple autonomous organizations. The purposes of this paper were to (a) demonstrate how RE-AIM can be operationalized and applied to evaluate the collective impact of similar autonomous programs that promote health and well-being and (b) provide preliminary data on the collective impact of Canadian spinal cord injury (SCI) peer mentorship programs on the delivery of peer mentorship services. METHODS: Criteria from all five RE-AIM dimensions were operationalized to evaluate multiple similar community-based programs. For this study, nine provincial organizations that serve people with SCI were recruited from across Canada. Organizations completed a structured self-report questionnaire and participated in a qualitative telephone interview to examine different elements of their peer mentorship program. Data were analyzed using summary statistics. RESULTS: Having multiple indicators to assess RE-AIM dimensions provided a broad evaluation of the impact of Canadian SCI peer mentorship programs. Peer mentorship programs reached 1.63% of the estimated Canadian SCI population. The majority (67%) of organizations tracked the effectiveness of peer mentorship through testimonials and reports. Setting-level adoption rates were high with 100% of organizations offering peer mentorship in community and hospital settings. On average, organizations allocated 10.4% of their operating budget and 9.8% of their staff to implement peer mentorship and 89% had maintained their programming for over 10 years. Full interpretation of the collective impact of peer mentorship programs was limited as complete data were only collected for 52% of survey questions. CONCLUSIONS: The lack of available organizational data highlights a significant challenge when using RE-AIM to evaluate the collective impact of multiple programs that promote health and well-being. Although researchers are encouraged to use RE-AIM to evaluate the collective impact of programs delivered by different organizations, documenting limitations and providing recommendations should be done to further the understanding of how best to operationalize RE-AIM in these contexts.
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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 | 0.118 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".