The Influence of Examiner Gender on Responses to Tonic Heat Pain Assessments: A Preliminary Investigation
Bibliographic record
Abstract
Background: The influence of examiner gender on pain reporting has been previously explored in both research and clinical settings. However, previous investigations have been limited, with the majority of studies employing single, static assessments of pain (e.g., cold pressor test, verbal pain ratings). The impact of examiner gender on both static and dynamic heat-based pain assessments is currently unknown. Methods: Thirty eight participants (20 females aged 24.1 ± 4.44, and 18 males, aged 24.8 ± 4.54) completed two identical testing sessions, randomized to a male and female examiner in a cross-over design. Pain sensitivity was examined using heat pain thresholds, verbal pain ratings to tonic heat, computerized visual analog scale (CoVAS) rating to tonic heat, and participant-controlled temperature (PCT) heat pain assessments. Results: Female participants reported higher verbal pain to tonic heat with a female examiner compared to male participants, with similar trends for CoVAS responses to tonic heat. Conversely heat pain thresholds and PCT were not significantly influenced by experimenter gender. Conclusions: Overall, verbal ratings were the most impacted by examiner gender, with temperature-based methods such as PCT and pain thresholds showing little to no examiner gender effects. While the gender of the examiner may be an important consideration in the measurement of sex and gender differences in pain research, the choice of pain assessment method may be of similar consequence.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".