Do discrimination, residential school attendance and cultural disruption add to individual-level diabetes risk among Aboriginal people in Canada?
Bibliographic record
Abstract
BACKGROUND: Aboriginal peoples in Canada (First Nations, Metis and Inuit) are experiencing an epidemic of diabetes and its complications but little is known about the influence of factors attributed to colonization. The purpose of this study was to investigate the possible role of discrimination, residential school attendance and cultural disruption on diabetes occurrence among First Nations adults. METHODS: This 2012/13 cross sectional survey was conducted in two Saskatchewan First Nations communities comprising 580 households and 1570 adults. In addition to self-reported diabetes, interviewer-administered questionnaires collected information on possible diabetes determinants including widely recognized (e.g. age, sex, lifestyle, social determinants) and colonization-related factors. Clustering effect within households was adjusted using Generalized Estimating Equations. RESULTS: Responses were obtained from 874 (55.7 %) men and women aged 18 and older living in 406 (70.0 %) households. Diabetes prevalence was 15.8 % among women and 9.7 % among men. In the final models, increasing age and adiposity were significant risk factors for diabetes (e.g. OR 8.72 [95 % CI 4.62; 16.46] for those 50+, and OR 8.97 [95 % CI 3.58; 22.52] for BMI 30+) as was spending most time on-reserve. Residential school attendance and cultural disruption were not predictive of diabetes at an individual level but those experiencing the most discrimination had a lower prevalence of diabetes compared to those who experienced little discrimination (2.4 % versus 13.6 %; OR 0.11 [95 % CI 0.02; 0.50]). Those experiencing the most discrimination were significantly more likely to be married and to have higher incomes. CONCLUSIONS: Known diabetes risk factors were important determinants of diabetes among First Nations people, but residential school attendance and cultural disruption were not predictive of diabetes on an individual level. In contrast, those experiencing the highest levels of discrimination had a low prevalence of diabetes. Although the reasons underlying this latter finding are unclear, it appears to relate to increased engagement with society off-reserve which may lead to an improvement in the social determinants of health. While this may have physical health benefits for First Nations people due to improved socio-economic status and other undefined influences, our findings suggest that this comes at a high emotional price.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".