[Co-construction of a program to promote community participation among seniors living with psychosocial issues, with or without mental health problems].
Bibliographic record
Abstract
Objectives A significant proportion of Quebec seniors are living with mental health problems or psychosocial issues such as isolation, bereavement, and psychological distress. These people face many forms of exclusion and are likely to have limited social participation. This paper describes the co-construction steps of a program aimed at promoting community participation among this population.Methods A method for the co-construction of innovative practices in health promotion was used to develop a program that is relevant, rigorous and feasible in diverse settings. The process included several steps, notably: need analysis among seniors and practitioners, development of a logical model for the program, preparation of the leader's manual, validation of the manual by experts, and pilot testing of the program among groups of seniors.Results The goal of the Count me in! program is to promote utilization of the resources of the community that can provide seniors living with mental health conditions or psychosocial issues with activities and positive social contact. The intervention is based on the Strength Model. It includes an individual interview, an eight-meeting workshop, visits to community resources, and collective production of media communication.Conclusion A co-construction process allowed the program to be continuously adjusted in response to stakeholders' feedback. The most important lever for the co-construction was the reconciliation of the partners' practical, conceptual, and experiential expertise. However, contextual factors such as the organization and the availability of mental health services for seniors constituted important barriers to the process.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".