Pathways to suicidality across ethnic groups in Canadian adults: the possible role of social stress
Bibliographic record
Abstract
BACKGROUND: Ethnicity is an important determinant of mental health outcomes including suicidality (i.e. suicidal ideation and suicide attempt). Understanding ethnic differences in the pathways to suicidality is important for suicide prevention efforts in ethnically diverse populations. These pathways can be conceptualized within a social stress framework. METHOD: The study examines ethnic differences in the pathways to suicidality in Canada within a social stress framework. Using data from the Canadian Community Health Survey Cycle 1.1 (CCHS 1.1) and path analysis, we examined the hypotheses that variations in (1) socio-economic status (SES), (2) sense of community belonging (SCB), (3) SES and SCB combined, and (4) SES, SCB and clinical factors combined can explain ethnic differences in suicidality. RESULTS: Francophone whites and Aboriginals were more likely to report suicidality compared to Anglophone whites whereas visible minorities and Foreign-born whites were least likely. Disadvantages in income, income and education, income and its combined effect with depression and alcohol dependence/abuse led to high rates even among the low-risk visible minority group. Indirect pathways for Asians differed from that of Blacks and South Asians, specifically through SCB. With the exception of SCB, Aboriginals were most disadvantaged, which exacerbated their risk for suicidality. However, their strong SCB buffered the risk for suicidality across pathways. Disadvantages in education, income and SCB were associated with the high risk for suicidality in Francophone whites. CONCLUSIONS: Francophone whites and Aboriginals had higher odds of suicidality compared to Anglophone whites; however, some pathways differed, indicating the need for targeted program planning and prevention efforts.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".