[Portrait of the Women's Mental Health who Consult Community Organization of a Quebec Region].
Bibliographic record
Abstract
In Québec, one in three people is at risk of being affected by a mental health problem during his lifetime. Women are twice as likely as men to suffer from mild mental health issues such as depression and anxiety. Poverty, violence and sexual abuse, difficulty to have access to adequate and affordable housing and poor working conditions are among the risk for women of being affected by a mental health problem. Objectives This study was conducted upon the request of a Réseau des groupes de femmes and provides a portrait of women's mental health who attend community organizations in a Quebec region. Method A quantitative descriptive research design was used to collect data guided by the theoretical model of Desjardins et al. (2008). A total of 171 volunteers from 16 different community organizations completed a self-administered questionnaire. Results The analysis of the data highlights the protective factors of mental health such as self-esteem and social support and risk factors such as low income and stressful life. It reveals that while the majority of the women have good mental health, some of them live with poorer mental health associated with several other factors, especially violence and poverty. Conclusion Results could lead to the development of actions meeting the specific needs of women living with poor mental health. This study also highlights the contribution of the community organizations for supporting the people living with mental health issues.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".