Underestimation of pain by health-care providers: towards a model of the process of inferring pain in others.
Bibliographic record
Abstract
Health professionals are routinely exposed to evidence of pain in others. It is important that the processes by which they evaluate pain be understood. The purposes of this article are to review and synthesize recent research on how health professionals judge the pain of others and to present a conceptual model of this process. Methodological and conceptual issues in the conduct of pain judgement studies are addressed. Research in this field over the last 40 years has indicated that, when compared with the pain judgements of patients themselves, health professionals tend to underestimate pain. The authors review the relation of this underestimation bias to such variables as the nature of the patient's pain and the clinical experience of the judge. They also review experiential and cognitive-perceptual variables found to influence the degree of underestimation bias, such as the amount of exposure to evidence of pain and suspicion about the motivations of the patient. A model of the pain decoding process is presented. The issue of whether underestimation has implications for treatment outcome is addressed and priorities for future research are identified.
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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 | 0.023 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".