Underestimation of pain by health-care providers: towards a model of the process of inferring pain in others.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.002 |
| 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 it