What are the factors associated with the implementation of a peer-led health promotion program? Insights from a multiple-case study
Bibliographic record
Abstract
Peer education is widely used as a health promotion strategy. However, few efforts have been undertaken to understand the implementation of peer-led health promotion programs (HPPs). This multiple-case study identifies factors facilitating the implementation of a peer-led HPP for older adults presenting with fear of falling (Vivre en �quilibre) and their mechanisms of action. It used a conceptual framework postulating factors that may influence peer-led HPPs implementation and mechanisms through which such factors may generate implementation outcomes. Six independent-living residences for older adults in Quebec (Canada) implemented Vivre en �quilibre as part of a quasi-experimental study. Implementation factors and outcomes were documented through observation diaries, attendance sheets, peers' logbooks, questionnaires administered to participants and semi-structured interviews conducted among peers, activity coordinators of residences and a subgroup of participants. The analysis revealed three categories of factors facilitating program implementation, related to individuals, to the program and to the organizational context. Three action mechanisms identified in the framework (interaction, self-organization and adaptation) were facilitated by some of these factors. These findings support the application of the peer-led program implementation conceptual framework used in this study and provide insights for practitioners and researchers interested in implementing peer-led HPPs.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".