Bibliographic record
Abstract
This study examined the relationship between race and mental health among Canadian adults. The purpose was to assess how social organization contributes to the racial distribution of mental health. The study defined mental health as a multi-dimensional construct that includes negative, positive, and subjective facets. The empirical analysis compared East Asians, South Asians, Blacks, Aboriginals, and mixed race persons to Whites on major depression, psychological distress, psychological well-being, and self-rated mental health. Separate comparisons were made for women and men because the relationship between race and mental health could be conditional on gender. Using individual-level data from the Canadian Community Health Survey (CCHS) 1.2 and aggregate data from the 2001 Canadian Census, the study hypothesized that racial differences in mental health could reflect differences in stress exposure, socioeconomic status, social embeddedness, and neighborhood environment. The main assumption was that higher stress exposure, economic hardship, social isolation, and neighborhood disadvantage could compromise the mental health of racial minorities. The study also examined whether social support and coping behaviors protected racial minorities from these health-damaging effects. The findings do not present a straightforward or a consistent set of conclusions. Although there is a good rational to believe that racial minorities should have worse mental health than Whites, this is not always or even mostly the case. Only Aboriginal women have a consistent disadvantage. For the most part, racial minorities have similar mental health as Whites, and even have an advantage in a few instances. Since the analysis covered the negative, positive, and subjective dimensions of mental health, it provides robust evidence to support this conclusion. However, the findings also demonstrate that low socioeconomic status and insufficient social resources can indeed have health-damaging effects. These factors explain some of the observed disadvantages in mental health that racial minorities experience or suppress an advantage among them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".