Local And System-Wide Considerations To Enhance Equitable Participation In Community-Based Older Adult Exercise Programs
Bibliographic record
Abstract
Group-based exercise programs for older adults produce substantial health benefits, yet they often fail to reach equity-deserving populations in representative proportions. An understanding of factors at local and broader contextual levels surrounding exercise programs is needed to guide strategies to reduce these gaps. PURPOSE: The purpose of the study was to understand the local and system-wide factors that influence equitable participation in the Forever...in motion (FIM) older adult exercise program in Saskatchewan, Canada. METHODS: We conducted semi-structured interviews with 17 participants who served as program implementers and/or exercise leaders in the FIM program in various locations across the province. Guided by the health equity implementation framework (HEIF), thematic analysis involved coding into categories of the HEIF and then inductively eliciting themes relevant to equity-focused implementation. RESULTS: Innovation characteristics such as low cost, adaptive group exercise in community settings and online options for attending training courses or exercise classes helped promote equity, as did leader and implementer characteristics and contextual factors that centered on community collaboration. Other HEIF domains negatively impacted equitable participation, including participant barriers relating to social determinants of health, leader and implementer lack of knowledge around strategies to promote equity, and contextual factors such as a lack of equity-focused goals or metrics or varied levels of implementation support across jurisdictions. Three inductively-derived themes for advancing equity included: establish a unified provincial FIM system, foster a culture of inclusive and culturally-centered collaboration and engagement, and develop equity-focused goals and metrics. CONCLUSION: Implementation frameworks such as the HEIF can provide exercise professionals with a comprehensive understanding of factors within exercise and other healthcare systems that impact equitable participation and can improve knowledge of system-wide strategies to reduce equity-related gaps. Supported by: University of Saskatchewan College of Medicine Research Award
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".