Exploring the role of gender and gendered pain expectation in physiotherapy students
Bibliographic record
Abstract
Introduction Gender and gender role pain expectations may influence how health care providers interact with and manage their patients’ symptoms.Purpose The purpose of this study was to describe gendered traits and gender role pain expectations among physical therapy students.Method A survey assessing gendered traits and gender role expectations in relation to pain was completed by a sample of 171 physical therapy students (120 women, 51 men). Data were analyzed using descriptive statistics and differences between men and women were tested with chi-square or Kruskal-Wallis.Results Men and women in physical therapy training were not different on 13 out of 16 of the gendered traits. The exceptions were that men rated themselves as more “decisive” compared to women (mean rank = 103.8 vs. mean rank = 78.4, P = 0.001) and women rated themselves as more “emotional” (mean rank = 91.95 vs. mean rank = 72.01, P = 0.009) and more “nurturing” (mean rank = 90.89 vs. mean rank = 72.91, P = 0.020).No significant differences were found in terms of gendered expectations of pain sensitivity, endurance, or in terms of personal experience of pain between the men and women in the sample. However, the majority (75%) of participants reported that women were more willing to report pain compared to men. Finally, both groups rated themselves as no different in handling pain compared to a typical man or woman.Conclusion In conclusion, men and women in training to be physical therapists demonstrate similar gendered trait profiles and little gender bias in relation to pain expectations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".