Does a diagnosis of depression influence observer ratings of pain severity? The mediating role of causal attributions of pain and pain genuineness
Bibliographic record
Abstract
Researchers have been increasingly investigating observer and patient characteristics that may influence the assessment of pain in others. While rates of psychiatric conditions are high in chronic pain populations, surprisingly little attention has been given to if (and why) a comorbid psychiatric diagnosis may influence the estimation of pain in others. Using an experimental vignette paradigm, the current study examined whether a diagnostic label of major depressive disorder (MDD) would impact observer pain estimates of a woman with chronic pain, and whether causal attributions of pain and pain genuineness might help explain these effects. Participants ( n = 188) were given a vignette describing a female patient with chronic pain (who either had MDD or no mental health concerns), viewed a brief video clip of the patient, and then were asked to provide a variety of ratings about the woman’s pain. Results of a serial multiple mediation analysis revealed that participants in the MDD condition made greater psychological attributions for the woman’s pain, which was associated with lower perceptions of pain genuineness, which was then associated with lower estimates of pain intensity. These findings suggest that a diagnosis of depression may indirectly influence observer estimates of another person’s pain by heightening psychological attributions of pain, and making their pain seem less genuine. Further research is needed to elucidate the complex processes underlying pain estimation, including patient and observer characteristics, biases, and heuristics, in order to improve quality of care for those living with persistent pain.’
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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 | 0.008 | 0.060 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".