Using mixed methods and partnership to develop a program evaluation toolkit for organizations that provide physical activity programs for persons with disabilities
Bibliographic record
Abstract
BACKGROUND: The purpose of this paper is to report on the process for developing an online RE-AIM evaluation toolkit in partnership with organizations that provide physical activity programming for persons with disabilities. METHODS: A community-university partnership was established and guided by an integrated knowledge translation approach. The four-step development process included: (1) identify, review, and select knowledge (literature review and two rounds of Delphi consensus-building), (2) adapt knowledge to local context (rating feasibility of outcomes and integration into online platform), (3) assess barriers and facilitators (think-aloud interviews), and (4) select, tailor, implement (collaborative dissemination plan). RESULTS: Step 1: Fifteen RE-AIM papers relevant to community programming were identified during the literature review. Two rounds of Delphi refined indicators for the toolkit related to reach, effectiveness, adoption, implementation, and maintenance. Step 2: At least one measure was linked with each indicator. Ten research and community partners participated in assessing the feasibility of measures, resulting in a total of 85 measures. Step 3: Interviews resulted in several recommendations for the online platform and toolkit. Step 4: Project partners developed a dissemination plan, including an information package, webinars, and publications. DISCUSSION: This project demonstrates that community and university partners can collaborate to develop a useful, evidence-informed evaluation resource for both audiences. We identified several strategies for partnership when creating a toolkit, including using a set of expectations, engaging research users from the outset, using consensus methods, recruiting users through networks, and mentorship of trainees. The toolkit can be found at et.cdpp.ca. Next steps include disseminating (e.g., through webinars, conferences) and evaluating the toolkit to improve its use for diverse contexts (e.g., universal PA programming).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".