The Disparagement of Pain: Social Influences on Medical Thinking
Bibliographic record
Abstract
Patients with pain often feel that their suffering is taken lightly, dismissed or denied. Before the introduction of anesthesia, pain was regarded as an awful affliction. This view diminished somewhat once anesthesia became available, although it still holds true for some forms of pain, eg, pain associated with terminal cancer. Pain was then treated as less troublesome when it became a reason for disability compensation to be paid. Examples are given of the disparagement of complaints by individuals reporting pain in the past 150 years. Factors that encourage doctors to underestimate patients′ pain include the requirement for doctors to control the issue of narcotics; circumstances in which patients may benefit from compensation by claiming that their pain is great; and the development of attitudes that understate the importance of the relief of pain and overstate the importance of activity, exercise and not complaining. Current attitudes in this respect are associated with the insurance industry, but it has been shown that, even patients who do not have a compensable injury or have pain that is not disabling fail to receive the treatment for pain that is appropriate, eg, postoperatively. The present paper reviews and discusses these problems and suggests that disparagement of pain and disability in the medicolegal field also leads to the rejection of pain in other contexts.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".