The social determinants of substance use for aboriginal women: A systematic review
Bibliographic record
Abstract
Although women who use substances are often also facing severe economic and social problems, little is known about the relationship between social determinants of health and substance use among women. Furthermore, despite their increased visibility in substance use programs and policies in Canada, little is known about the social contexts of substance use among Aboriginal women. I systematically reviewed empirical research published from 1997 through March 2013 that examined the relationship between social determinants of health and substance use among Aboriginal women. Studies that were peer-reviewed, published in English, and had an abstract were included. Of an initial 261 studies, only sixteen studies met the inclusion criteria (fourteen quantitative, one qualitative, one mixed methods). The social determinants of health that were explored in these studies were socio-demographics factors, trauma, gender, social environments, colonialism, culture, and employment. The studies identified significant relationships between the social determinants of health and substance use among Aboriginal women. The almost exclusive use of quantitative methods and the prioritization of certain social determinants of health over others prevented a comprehensive and contextual understanding of substance use among Aboriginal women. Further research is needed to understand these significant relationships, particularly in relation to Aboriginal-specific determinants of health.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".