Review of chronic non-cancer pain research among Aboriginal people in Canada
Bibliographic record
Abstract
PURPOSE: Aboriginal people in Canada are disproportionately affected by chronic illnesses, compared to non-Aboriginal Canadians. The purpose of this review was to determine whether differences exist between the two groups with respect to chronic non-cancer pain (CNCP) in order to better inform clinical practice and to identify research gaps. DATA SOURCES: Four electronic databases were searched for the period of 1990-2015. STUDY SELECTION: Only English and French language original studies that examined CNCP prevalence, assessment tools and beliefs among Aboriginal people in Canada were considered. DATA EXTRACTION: Data extracted included Aboriginal group, geographic location, study setting and pain definition (for prevalence studies only). RESULTS OF DATA SYNTHESIS: A total of 11 studies matched the selection criteria: 10 reported estimates of chronic pain prevalence among Aboriginal people in Canada, 1 was about a culturally adapted pain assessment tool, and no study was found about CNCP beliefs within Aboriginal people. CONCLUSION: CNCP among Aboriginal people is still a largely unexplored research field. The limited evidence available so far does not allow us to conclude that CNCP affects a higher proportion of Aboriginal than non-Aboriginal people in Canada. However, arthritis, a specific condition associated with chronic pain, is more prevalent in Aboriginal than non-Aboriginal people. Additional research is needed on other CNCP types and conditions. Furthermore, pain assessment tools are not culturally adapted and clinicians should inquire more about the beliefs of Aboriginal patients to make them feel safer and to better target 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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.017 | 0.035 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".