Suicidal Ideation: The Role of Economic and Aboriginal Cultural Status after Multivariate Adjustment
Bibliographic record
Abstract
OBJECTIVE: To determine if Aboriginal (in this paper, First Nations and Métis people) cultural status is independently associated with lifetime suicidal ideation in the Saskatoon Health Region after controlling for other covariates, particularly income status. METHODS: Data collected by Statistics Canada in all 3 cycles of the Canadian Community Health Survey (CCHS) were merged with identical questions asked in February 2007 by the Saskatoon Health Region. The health outcome was lifetime suicidal ideation. The risk indicators included demographics, socioeconomic status, cultural status, behaviours, life stress, health care use, and other health problems. RESULTS: Participants (n = 5948) completed the survey with a response rate of 81.1%. The prevalence of lifetime suicidal ideation was 11.9%. After stratification, it was found that high-income Aboriginal people have similar low levels of suicidal ideation, compared with high-income Caucasian people. The risk-hazard model demonstrated a larger independent effect of income status in explaining the association between Aboriginal cultural status and lifetime suicidal ideation, compared with the independent effect of age. After full multivariate adjustment, Aboriginal cultural status had a substantially reduced association with lifetime suicidal ideation. The odds of lifetime suicidal ideation for Aboriginal people reduced from 3.28 to 1.99 after multivariate adjustment for household income alone. CONCLUSION: The results of this study suggest reductions in lifetime suicidal ideation can be observed in Aboriginal people in Canada by adjusting levels of household income.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".