Exploring the contributions of sex and traditionally genderized interpersonal-expressive traits to variability in post-trauma pain ratings
Bibliographic record
Abstract
OBJECTIVES: Multiple intra- and inter-individual variabilities sculpt the experience of pain. However, integration of sex and gender has been under-explored in explanatory models of pain. This study aimed to examine the role of sex and traditionally genderized interpersonal-expressive traits, and their interactions in explaining the variability of pain ratings. METHODS: Data from 113 participants following acute non-catastrophic musculoskeletal (MSK) injuries were included. Participants completed the Brief Pain Inventory (BPI) and the Gender, Pain and Expectations Scale (GPES). An independent T-test was used to compare differences in BPI subscales between the sexes. Pearson correlations explored the associations between BPI and GPES subscale scores for the overall sample and also for the sample when disaggregated by sex. Multiple linear regression was used to investigate the interaction of sex and gender traits in explaining the BPI scores. RESULTS: No differences were found between the sexes in mean BPI Severity and Interference. Across sexes, Relationship-oriented was positively associated with greater BPI Severity (r = 0.20) and Emotive was positively associated with BPI Interference (r = 0.24). In sex-disaggregated analyses, these associations were significant in females only. Goal-oriented was associated with neither BPI Severity nor Interference. In multivariate regression, only Emotive was a significant predictor of BPI Interference. DISCUSSION: The findings suggest that variances in pain-related interference are partially explained by scores on a scale measuring self-perceptions of Emotive qualities. Sex was not predictive of either pain outcome in both bivariate and multivariate analyses. Researchers and clinicians are encouraged to consider both sex- and gender-based variables when interpreting patient pain reports.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".