Exploring relationships between socioeconomic position, family context, culture, and suicidality among Métis peoples : reflections from the 2006 Aboriginal Peoples Survey
Bibliographic record
Abstract
According to a 2003 Health Canada report, suicide was the leading cause of death among Aboriginal individuals under the age of 45, accounting for 23% of all deaths in this at-risk population. While previous research has explored many potential risk factors for suicide among Aboriginal populations, none have considered the Métis population independent of other Aboriginal groups. Additionally, there have been no studies explicitly examining the relationship between family context and suicidality among either of these populations; this is the primary relationship of interest in this project. Data used for this project was taken from the 2006 Aboriginal People’s Survey (APS). The APS is a national cross-sectional survey of 61,041 First Nations, Inuit and Métis peoples. Within the APS, family context was constructed using several variables including parental divorce, childhood adoption, number of siblings, etc. Analyses for this project included a multi-stage process consisting of bivariate and multivariable analyses. Multivariable logistic regression analysis was separated by gender and examined those aged 25-54. Results showed that that for women, renting versus owning your home, the death of sibling under age 2, or being removed by a child welfare agency, the church, or government officials was significantly associated with suicidal ideation. For men, unemployment, living in the community of origin, death of a sibling under age 2, and participating in traditional craftwork all significantly associated with suicidal ideation. Not graduating from high school and unemployment were significantly associated with suicide attempts for men or women when controlling for all other demographic, family context, and culture variables within the final model. As has been the case in previous research surrounding culture, several of the results in the bivariate analysis of this project were counterintuitive (Wilson & Rosenberg, 2002). This shows that nuanced and contextual interpretations are critical, and a space is opened with this research to critically consider what exactly is being captured through the survey measures. I argue that the strength of the linkage between a measure and its conceptual basis becomes increasingly tenuous and problematic as the complexity of the circumstance the measure is attempting to capture increases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".