Exploring the Domains of Gender as Measured by a New Gender, Pain and Expectations Scale
Bibliographic record
Abstract
Background: While sex- or gender-based differences in pain expression have been documented, exploration of traditionally genderized traits on pain has been hampered by the lack of strong measurement tools. This study evaluated the structural validity of a 16-item “Gender personality traits” subscale of a recently developed Gender, Pain and Expectations Scale (GPES). Methods: Data were drawn from an existing database of 248 participants (65.7% female). Maximum likelihood-based confirmatory factor analysis was carried out while considering the conceptual meaningfulness of subscales to evaluate the factor structure identified by these traits. Construct validity was explored using a priori hypotheses regarding anticipated mean differences in scores between biological male and female participants. Results: A meaningful factor structure could not be defined with all 16 items. Through conceptual and statistical triangulation a three-factor structure informed by 10 items was identified that satisfied acceptable fit criteria. The factors were termed “Emotive,” “Relationship-Oriented,” and “Goal-Oriented.” Evidence of construct validity was supported through significant sex-based differences (p ≤ 0.02) in the expected directions for all three subscales. Conclusions: Review of the items in the three factors led the researchers to endorse a move away from naming these “masculine” and “feminine,” rather focusing on the nature of the traits: “Relationship-oriented,” “Emotive,” and “Goal-oriented.” Implications for researchers conducting sex/gender-based pain research are discussed. Clinical Trial Registration number: NCT02711085.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".