Exploring the relationship between male norm beliefs, pain‐related beliefs and behaviours: An online questionnaire study
Bibliographic record
Abstract
BACKGROUND: Gender beliefs help explain the variation found in pain among men and women. Gender norms and expectations are thought to affect how men and women report and express pain. However, less is known about how such beliefs are related to pain outside of laboratory settings. The aim of this study was therefore to consider the relationship between beliefs in male role norms, pain and pain behaviours in men and women. METHODS: An online questionnaire study was conducted. A total of 468 adults (352 women), with or without pain, completed a series of self-report measures relating to beliefs about pain and male role norms, as well as pain and general health behaviours. RESULTS: An experience of pain was associated with lower beliefs in traditional male norms. Endorsing stereotypical male norms was related to increased stigma associated with seeking professional help for pain in both men and women, but to a lesser extent associated with general health behaviours. There also seemed to be gender-based beliefs associated with the expression of pain. CONCLUSIONS: Together these findings suggest that beliefs in gender (male) norms are relevant to pain, and that there is utility in exploring the variation in pain beyond binary male-female categories.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".