Prevalence and factors associated with pain-related disabilities among Inuit in Canada in 2017: a cross-sectional study
Bibliographic record
Abstract
Working with an Indigenous Advisory Committee, including an Inuit Health Advisor and Researcher, we analyzed the 2017 Aboriginal Peoples Survey to examine prevalence and factors associated with pain-related disabilities (PRDs) among Inuit in Canada. Self-reported data were collected from Inuit ≥15 years. PRDs were defined as 'sometimes', 'often', or 'always' experiencing activity limitations due to pain from a long-term condition lasting ≥ six months. We computed PRD prevalence [95% CI] overall, and by geographic location, age, sex, type and number of co-existing disabilities. Modified Poisson regression with robust variance estimation modelled associations between Inuit social determinants of health and PRDs. Person-level and bootstrap weights were applied for all analyses. Among Inuit, 11.1% [10.0, 12.4] reported PRDs. Females [13.4% (11.8, 15.1)], individuals 55 + [23.7% (21.6, 25.9)], and those who lived outside Inuit Nunangat [17.1% (14.1, 20.5)] experienced higher prevalence of PRDs. Prevalence increased with the number of disabilities-highest among those with co-existing physical disabilities. Additionally, higher education, residential school attendance, and those who experienced difficulties related to food, housing, employment, and health were more likely to report PRDs. Characteristics which may increase the risk of PRDs need to be shared with Inuit stakeholders to guide next steps for awareness, advocacy, services and interventions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".