Vulnérabilités psychosociales des populations rurales du Québec en temps de pandémie
Bibliographic record
Abstract
INTRODUCTION: Psychosocial impacts of the coronavirus (COVID-19) pandemic, including those on mental health, are now recognized. However, the experience of the COVID-19 pandemic differs from one individual, group or context to another and solutions to cope with it must be adapted and contextualized. AIM OF THE STUDY: This study aims to identify factors of psychosocial vulnerability in rural populations in Quebec (Canada). METHOD: The approach is adapted from previous work on the prevention and reduction of the psychosocial impacts of climate change in non-metropolitan areas. A descriptive qualitative design based on several data sources was used. The data come from a press review, a review of the scientific literature, semi-structured interviews with key actors in the community and municipal domains. RESULTS: Data triangulation and validation by community organization teams (public health department) identified forty-one (N = 41) factors (e.g., social cohesion, digital literacy) likely to increase or decrease the psychosocial vulnerability of rural populations in the context of a pandemic. These factors are grouped under six categories of determinants: 1) population's knowledge and attitudes towards the pandemic, 2) previous experience of difficult events, 3) community dynamism, social cohesion and solidarity, 4) citizens, municipalities and government authorities' involvement, 5) health and social services and those from their intersectoral partners, and 6) land use planning. CONCLUSION: These results are useful for local and regional public health teams in developing local portraits of psychosocial vulnerabilities to support plans to strengthen community resilience and reduce social and health inequalities accentuated by the pandemic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".