Testing two accounts of pain underestimation
Bibliographic record
Abstract
Two important influences on pain underestimation by health care professionals were investigated by varying specific cues with reference to underestimation of patients' pain: when observers are not allowed to talk to patients and when observers expect social cheating. One hundred and twenty health care professionals watched videotaped facial expressions of pain patients and estimated their pain. The first group only saw the faces, the second group was given patients' self-reports in addition and the last group was given a context cue priming them to expect cheating in addition to faces and patients' ratings. Health care professionals generally underestimated patients' pain, but this varied depending on the cues given. Those viewing the face without patients' ratings underestimated pain to a greater extent than health care professionals provided with patients' ratings. Health care professionals primed to expect cheating underestimated pain as much as those seeing only patients' faces. Therefore, both accounts, verbal report as important but missing cue as well as an alerted cheating detection device, could account for underestimation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".