A scoping review : physical pain among Indigenous Peoples in Canada
Bibliographic record
Abstract
Purpose: Pain is a multifaceted experience shaped by various factors including context of pain, previous life events, and ongoing ethnocultural circumstances. Moreover, pain’s definition is inconsistent across cultures. Western medicine views physical pain (e.g., fractured bone) and non-physical pain (e.g., depression) as two distinct conditions. Indigenous perspectives are often more wholistic, encompassing mental, spiritual, emotional, and physical hurt. The subjective nature of pain invites ample opportunity for discrimination in both its assessment and management. As such, it is important to consider Indigenous perspectives of pain in research and clinical practice. To investigate what aspects of Indigenous pain knowledge is currently considered by Western research, we conducted a scoping review on pain among Indigenous Peoples of Canada. Methods: In June 2021, 9 databases were searched with 8,220 papers downloaded after duplicates removed. Abstract and full-text screening was conducted by two independent reviewers. Principle Findings: 77 papers were included for analysis. Using grounded theory, five themes emerged: pain measures/scales (n=7), interventions (n=13), pharmaceuticals (n=17), pain expression/experiences (n=45), and pain conditions (n=70). The lack of research in pain measurement and scales (n=7) is discouraging, considering the emerging perspective that Indigenous peoples perceive their pain as ignored, minimized, or disbelieved. Conclusions drawn from the pain expression and experiences theme also highlight a divide between pain expression in Indigenous peoples and pain assessment in medical professionals. Conclusion: The limited research on pain measurement is discouraging in light of numerous studies reporting Indigenous Peoples experience their pain is ignored, minimized, or disbelieved. Furthermore, a clear disconnect emerged between pain expression in Indigenous Peoples and assessment in medical professionals. Overall, this review intends to translate current knowledge to other non-Indigenous academics and to initiate meaningful collaboration with Indigenous partners. Future research led by Indigenous academics and community partners is critically needed to better address pain needs in Canada.
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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.021 | 0.040 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".