Attending to Marginalization in The Chronic Pain Literature: A Scoping Review
Bibliographic record
Abstract
Background: There has been a recent and, for many within the chronic pain space, long-overdue increase in literature that focuses on equity, diversity, inclusion, and decolonization (EDI-D) to understand chronic pain among people who are historically and structurally marginalized. Aims: In light of this growing attention in chronic pain research, we undertook a scoping review of studies that focus on people living with chronic pain and marginalization to map how these studies were carried out, how marginalization was conceptualized and operationalized by researchers, and identify suggestions for moving forward with marginalization and EDI-D in mind to better support people living with chronic pain. Methods: We conducted this scoping review using critical analysis in a manner that aligns with dominant scoping review frameworks and recent developments made to scoping review methodology as well as reporting guidelines. Results: Drawing on 67 studies, we begin with a descriptive review of the literature followed by a critical review that aims to identify fissures within the field through the following themes: (1) varying considerations of sociopolitical and socioeconomic contexts, (2) conceptual conflations between sex and gender, and (3) differing approaches to how people living with chronic pain and marginalization are described. Conclusion: By identifying strengths and limitations in the research literature, we aim to highlight opportunities for researchers to contribute to a more comprehensive understanding of marginalization in chronic pain experiences.
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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 | 0.086 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.034 | 0.033 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".