Negative Social Interactions at the Intersection of Gender, Race and Immigration Status in Canada
Bibliographic record
Abstract
Abstract Negative social interactions (NSIs), defined as upsetting interpersonal encounters in daily life, are associated with adverse mental health conditions. Guided by an intersectional perspective, this study explored the impacts of gender, race and immigration status on the experiences of NSIs, using nationally representative data from the 2012 Canadian Community Health Survey-Mental Health (CCHS-MH). The sample consisted of 21,932 participants across Canada. Gender-specific multivariable logistic regression models were used to estimate the effects of race, immigrant status and the interaction term on the likelihood to experience NSIs. Study results showed that women (32.3 per cent) reported significantly more NSIs than men (25.4 per cent). For men, being an immigrant was significantly associated with a lower likelihood of experiencing NSIs; race did not have a significant effect on NSIs. Furthermore, the results revealed that racialised Canadian-born women were more likely to report NSIs than racialised immigrant women, whilst immigration status had no effect among white women. This study suggests the distinct influences of intersecting identities of race, gender and immigration status and that social workers should incorporate an intersectional lens when exploring clients’ social relationships and environments.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".