Levers for Change and Unexpected Outcomes of a Participatory Research Partnership: Toward Fostering Older adults’ Social Participation to Promote Health Equity
Bibliographic record
Abstract
Promoting health equity in aging requires ensuring older adults' effective access to community-based services fostering their social participation. This study aimed to (a) identify levers for change in community-based services to foster older adults' social participation and (b) explore unexpected outcomes of stakeholder engagement. Based in a large Canadian city, a critical participatory research partnership was formed in a district experiencing considerable health disparities. Four focus groups and seven individual interviews were followed by a collaborative workshop with 28 community stakeholders. Participants identified mainly systemic and organizational levers for change. These levers comprised changing performance indicators and the institutional culture of homecare to value services fostering social participation opportunities. Other levers included supporting individual change agency through participatory research involving community members. Stakeholder engagement led to five unexpected outcomes: "Marking a new beginning," "Expressing ourselves," "Feeling better," "Working together," and "Influencing the community." Recognizing levers for change is essential to understand how to develop services fostering social participation to promote health equity, with whom and in which 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 | 0.136 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| 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".