Associations of Gender Role and Pain in Musculoskeletal Disorders: A Mixed-Methods Systematic Review
Bibliographic record
Abstract
Previous studies have investigated the association of gender roles with pain outcomes in healthy individuals. However, little is known about this association in those with musculoskeletal (MSK) disorders. Therefore, this mixed-methods systematic review aimed to investigate the association of sociocultural gender roles on pain outcomes in adults with MSK disorders. Literature from Medline, CINAHL, Web of Science, and Embase was reviewed from inception to February 2023. Eligibility criteria included studies of adults with an MSK pain disorder that explored the relationship between gender roles and pain for all primary qualitative and quantitative study designs. Exclusion criteria were gray literature, review articles, case studies, and conference proceedings. Risk of bias was assessed via the Quality Appraisal for Diverse Studies for quantitative studies and the McMaster Quality Appraisal Tool for qualitative studies. Eleven studies were included, 9 qualitative, and 2 quantitative with a total of 540 participants (19.6% women, 80.4% men) with various MSK disorders. The convergent integrated approach was used to synthesize data from the qualitative and quantitative studies resulting in 3 themes and 7 subthemes. Our findings identified differences in the way individuals explained the cause of their pain, were treated for their pain in a social and systemic context, and in describing the effect pain had on their lives based on gender roles. There is a need for pain management to evolve to acknowledge the individual pain experience through exploration of an individual's gender identity and roles. PERSPECTIVE: This article demonstrates that gender roles have a multidimensional influence on the pain experience in those with MSK disorders. These findings support the development of gender-sensitive, patient-centered approaches to pain management, acknowledging each individual's important roles and identities.
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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 | 0.022 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".