Social prescribing needs and priorities of older adults in Canada: a qualitative analysis
Bibliographic record
Abstract
INTRODUCTION: Psychological well-being (PWB) is an important component of positive mental health (PMH) and an asset for population health. This study examined correlates of PWB among community-dwelling adults (18+ years) in the 10 Canadian provinces. METHODS: Using data from the 2019 Canadian Community Health Survey Rapid Response on PMH, we conducted linear regression analyses with sociodemographic, mental health, physical health and substance use variables as predictors of PWB. PWB was measured using six questions from the Mental Health Continuum-Short Form, which asked about feelings of self-acceptance, personal growth, environmental mastery, autonomy, positive relations and purpose in life during the past month. RESULTS: In unadjusted and adjusted analyses, older age, being married or in a commonlaw relationship and having a BMI in the overweight category (25.00-29.99) were associated with higher PWB, while reporting a mood disorder, anxiety disorder, high perceived life stress, engaging in heavy episodic drinking and frequent cannabis use were associated with lower PWB. Sex, having children living at home, immigrant status, racialized group membership, educational attainment, household income tertile, having a BMI in the obese category (≥30.00), major chronic disease and smoking status were not significantly associated with PWB. CONCLUSION: This research identifies sociodemographic, mental health, physical health and substance use factors associated with PWB among adults in Canada. These findings highlight groups and characteristics that could be the focus of future research to promote PMH.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".