Self-conscious emotions in patients suffering from chronic musculoskeletal pain: A brief report
Bibliographic record
Abstract
OBJECTIVE: The role of self-conscious emotions (SCEs) including shame, guilt, humiliation and embarrassment are of increasing interest within health. Yet, little is known about SCEs in the experience of chronic pain. This study explored prevalence and experience of SCEs in chronic pain patients compared to controls and assessed the relationship between SCEs and disability in pain patients. DESIGN AND MEASURES: Questionnaire assessment comparing musculoskeletal pain patients (n=64) and pain-free control participants (n=63). Pain was assessed using the McGill Pain Questionnaire; disability, using the Roland-Morris Disability Questionnaire; and six SCEs derived from three measures (i) Test of Self-Conscious Affect-3 yielding subscales of shame, guilt, externalisation and detachment (ii) The Brief Fear of Negative Evaluation Scale and (iii) The Pain Self-Perception Scale assessing mental defeat. RESULTS: Significantly greater levels of shame, guilt, fear of negative evaluation and mental defeat were observed in chronic pain patients compared to controls. In the pain group, SCE variables significantly predicted affective pain intensity; only mental defeat was significantly related to disability. CONCLUSION: Findings highlight the prevalence of negative SCEs and their importance in assessment and management of chronic pain. The role of mood in this relationship is yet to be explored.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".