Barriers and Facilitators to Implementing Exercise is Medicine Canada on Campus Groups
Bibliographic record
Abstract
The Exercise is Medicine Canada on Campus (EIMC-OC) program was established in 2013 to provide opportunities for students to promote physical activity in their campus communities. Currently, 38 EIMC-OC groups are in operation, and each has encountered challenges and enablers that have yet to be formally documented. This project aimed to (1) identify barriers and facilitators when implementing an EIMC-OC group and (2) investigate levels of implementation at which the barriers and facilitators operate. Throughout winter 2016, 22 EIMC-OC group leaders representing 12 groups contributed data. Participants completed a survey and a semistructured interview developed using the Consolidated Framework for Implementation Research (CFIR). Interviews were transcribed and underwent thematic analysis. Eighteen barriers and 24 facilitators were identified, with four influencers cited as both a barrier and a facilitator. Common barriers included group member time constraints and communicating with health care professionals. Common facilitators included collaborating with other groups and advertising. Most influencers corresponded to the inner setting and process CFIR domains. Findings from this study suggest that EIMC-OC groups face similar barriers and facilitators despite varying local contexts. The influencers identified highlight recommendations to enhance the success of the EIMC-OC program and other multisite health initiatives at academic institutions.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".