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Record W63422864 · doi:10.1155/2000/565309

The Disparagement of Pain: Social Influences on Medical Thinking

2000· article· en· W63422864 on OpenAlexaff
Harold Merskey, Robert Teasell

Bibliographic record

VenuePain Research and Management · 2000
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsWestern University
Fundersnot available
KeywordsCompensation (psychology)Pain and sufferingCancer painPsychologyPain catastrophizingPain controlFinancial compensationMedicinePsychiatryChronic painCancerSocial psychologyAnesthesiaLawPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.045
GPT teacher head0.371
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations32
Published2000
Admission routes1
Has abstractyes

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