Health-related quality of life for First Nations and Caucasian women in the First Nations Bone Health Study
Bibliographic record
Abstract
OBJECTIVE: Studies about the health of Indigenous (i.e., original inhabitants) populations often focus on chronic diseases and risk behaviors, emphasizing physical aspects of health. Our objective was to test for differences in self-reported health-related quality of life (HRQOL), which provides a multidimensional and holistic perspective on health, between First Nations (one group of Indigenous peoples) and Caucasian women. Data were from the First Nations Bone Health Study, conducted in the Canadian province of Manitoba. HRQOL was measured using the validated Medical Outcomes Study 36-Item Short Form Health Survey (SF-36). It captures respondent's perceptions of eight health domains, as well as overall mental and physical health components. RESULTS: Analyses were conducted for 707 participants of which 47.4% were of First Nations origin. First Nations respondents had significantly lower unadjusted scores (p < 0.05) than Caucasian respondents on all SF-36 dimensions, except bodily pain and vitality. They also had significantly lower overall mental health scores. After adjusting for multiple determinants of health (e.g., age, education, substance use), differences were no longer statistically significant, except for the social functioning and role emotional domains and overall mental health component. Complex cultural factors are likely responsible for the persistent mental health inequalities experienced by First Nations women.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".