COMMUNAL SUPPORT PREDICTS BETTER MENTAL HEALTH: KNOWLEDGE TRANSLATION AMONG OLDER ADULTS DURING COVID-19
Bibliographic record
Abstract
Abstract The well-being of older adults has been linked to the quality of their neighbourhood environment. Given that COVID-19 affected poorer neighbourhoods disproportionately, we partnered with community organizations to identify meso-level psychosocial factors that may improve loneliness, depressive mood, and cognitive function. Five variables were identified through focus groups with older adults and community organizations. These variables were drawn from validated scales, including communal provisions, neighbourhood friendship, self-expression, social experiences, and time outdoors. This paper presents preliminary findings from surveys administered to 151 community-dwelling older adults across British Columbia and interviews in four neighbourhoods.Purposeful and snowball sampling were used to recruit older adults (age 55+) from community centres and neighbourhood houses. Online surveys measured the five meso-level psychosocial exposure variables. Outcome variables included an index of loneliness, depressive mood, self-rated memory, semantic fluency and delayed recall. Data was geocoded and aggregated by Forward Sortation Area. Regression and cross-level mediation analysis were conducted. Four neighbourhoods were selected from a 2x2 matrix of high and low neighbourhood deprivation (CANUE, 2016). Mental health was associated with better social experiences (B=.26, p=.003). Time outdoors (B=.35, p=.047) was associated with better delayed recall. Mental health was better in poorer neighbourhoods (B=.20, p=.015). This was partially mediated by communal provisions (B=.19, p=.032). Social experiences (B=.23, p=.009) fully mediated these effects on mental health. Participants described being of local community services and took on opportunities to volunteer. Social experiences and neighbourhood resources may help support mental health and well-being among older adults during the pandemic and beyond.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".