Health care providers' judgments in chronic pain: the influence of gender and trustworthiness
Bibliographic record
Abstract
Estimates of patients' pain, and judgments of their pain expression, are affected by characteristics of the observer and of the patient. In this study, we investigated the impact of high or low trustworthiness, a rapid and automatic decision made about another, and of gender and depression history on judgments made by pain clinicians and by medical students. Judges viewed a video of a patient in pain presented with a brief history and rated his or her pain, and the likelihood that it was being exaggerated, minimized, or hidden. Judges also recommended various medical and treatment options. Contrary to expectations, trustworthiness had no main effect on pain estimates or judgments, but interacted with gender producing pervasive bias. Women, particularly those rated of low trustworthiness, were estimated to have less pain and to be more likely to exaggerate it. Unexpectedly, judgments of exaggeration and pain estimates were independent. Consistent with those judgments, men were more likely to be recommended analgesics, and women to be recommended psychological treatment. Effects of depression history were inconsistent and hard to interpret. Contrary to expectations, clinicians' pain estimates were higher than medical students', and indicated less scepticism. Empathy was unrelated to these judgments. Trustworthiness merits further exploration in healthcare providers' judgments of pain authenticity and how it interacts with other characteristics of patients. Furthermore, systematic disadvantage to women showing pain is of serious concern in healthcare settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".