CONNECTING ARTFULLY TOWARD PROMOTING THE MENTAL HEALTH OF IMMIGRANT WOMEN IN CANADA: A LITERATURE REVIEW
Bibliographic record
Abstract
Although Canada is a popular destination for immigrants, studies of the mental health of immigrant women in Canada have shown some of migration’s potential negative impacts. These studies also showcase how social support through community engagement can improve and maintain mental health by reducing immigrant women’s hopelessness and boosting their resilience. Additionally, because it promotes self-expression and self-confidence, engagement with art can be beneficial for health outcomes. Community arts programs serve as a space for creating artworks and exchanging social support and can have a positive psychological influence on participants. For instance, community arts programs can facilitate participants’ self-expression and the development of new friendships and artistic skills. Despite the existence of diverse community arts programs across Canada, there are few Canadian-focused scholarly studies investigating how engagement with these programs may contribute to participants’ mental health. In this paper, I investigate the association between engagement with art and its mental health–related outcomes among immigrant women in Canada. To explore this relationship, I provide a review of the scholarly literature on artmaking and mental health among immigrant women in Canada and assess how community art programs may impact their mental health by offering opportunities for social support exchanges. This paper argues that community art programs foster space for promoting mental health among immigrant women, and provides suggestions for researchers, mental health professionals, settlement service providers, and policy-makers in the field of migration.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".