Association of source country gender inequality with experiencing assault and poor mental health among young female immigrants to Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Gender inequality varies across countries and is associated with poor outcomes including violence against women and depression. Little is known about the relationship of source county gender inequality and poor health outcomes in female immigrants. METHODS: We used administrative databases to conduct a cohort study of 299,228 female immigrants ages 6-29 years becoming permanent residence in Ontario, Canada between 2003 and 2017 and followed up to March 31, 2020 for severe presentations of suffering assault, and selected mental health disorders (mood or anxiety, self-harm) as measured by hospital visits or death. Poisson regression examined the influence of source-country Gender Inequality Index (GII) quartile (Q) accounting for individual and country level characteristics. RESULTS: Immigrants from countries with the highest gender inequality (GII Q4) accounted for 40% of the sample, of whom 83% were from South Asia (SA) or Sub-Saharan Africa (SSA). The overall rate of assault was 10.9/10,000 person years (PY) while the rate of the poor mental health outcome was 77.5/10,000 PY. Both GII Q2 (Incident Rate Ratio (IRR): 1.48, 95% Confidence Interval (CI): 1.08, 2.01) and GII Q4 (IRR: 1.58, 95%CI: 1.08, 2.31) were significantly associated with experiencing assault but not with poor mental health. For females from countries with the highest gender inequality, there were significant regional differences in rates of assault, with SSA migrants experiencing high rates compared with those from SA. Relative to economic immigrants, refugees were at increased risk of sustaining assaults (IRR: 2.96, 95%CI: 2.32, 3.76) and poor mental health (IRR: 1.73, 95%CI: 1.50, 2.01). Higher educational attainment (bachelor's degree or higher) at immigration was protective (assaults IRR: 0.64, 95%CI: 0.51, 0.80; poor mental health IRR: 0.69, 95% CI: 0.60, 0.80). CONCLUSION: Source country gender inequality is not consistently associated with post-migration violence against women or severe depression, anxiety and self-harm in Ontario, Canada. Community-based research and intervention to address the documented socio-demographic disparities in outcomes of female immigrants is needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".