Social support, social conflict, and immigrant women's mental health in a <scp>C</scp>anadian context: a scoping review
Bibliographic record
Abstract
ACCESSIBLE SUMMARY: Social support has positive and negative dimensions, each of which has been associated with mental health outcomes. Social networks can also serve as sources of distress and conflict. This paper reviews journal articles published during the last 24 years to provide a consolidated summary of the role of social support and social conflict on immigrant women's mental health. The review reveals that social support can help immigrant women adjust to the new country, prevent depression and psychological distress, and access care and services. When social support is lacking or social networks act as a source of conflict, it can have negative effects on immigrant women's mental health. It is crucial that interventions, programmes, and services incorporate strategies to both enhance social support as well as reduce social conflict, in order to improve mental health and well-being of immigrant women. ABSTRACT: Researchers have documented the protective role of social support and the harmful consequences of social conflict on physical and mental health. However, consolidated information about social support, social conflict, and mental health of immigrant women in Canada is not available. This scoping review examined literature from the last 24 years to understand how social support and social conflict affect the mental health of immigrant women in Canada. We searched MEDLINE, PsycINFO, CINAHL, Healthstar, and EMBASE for peer-reviewed publications focusing on mental health among immigrant women in Canada. Thirty-four articles that met our inclusion criteria were reviewed, and are summarized under the following four headings: settlement challenges and the need for social support; social support and mental health outcomes; social conflict and reciprocity; and social support, social conflict, and mental health service use. The results revealed that social support can have a positive effect on immigrant women's mental health and well-being, and facilitate social inclusion and the use of health services. When social support is lacking or social networks act as a source of conflict, it can have negative effects on immigrant women's mental health. The results also highlighted the need for health services to be linguistically-appropriate and culturally-safe, and provide appropriate types of care and support in a timely manner in order to be helpful to immigrant women.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".