Imagery and Pain: The Prevalence, Characteristics, and Potency of Imagery Associated with Pain
Bibliographic record
Abstract
BACKGROUND: There is a dearth of information about imagery in pain sufferers. AIM: The aim of this study was to collect data on the characteristics, prevalence, and potency of imagery associated with pain. METHOD: The images of 59 pain sufferers were assessed by means of a semi-structured interview. The emotional, cognitive, behavioural, and pain-inducing properties (potency) of their index images were assessed by an image induction procedure and self-report scales of anxiety, depression and trauma symptoms. RESULTS: The results showed a remarkably high incidence of images in pain sufferers, with 78% of participants reporting one or more repetitive images when in pain. Exposure to their most powerful/distressing image (Index image) resulted in significant increases in negative emotions, negative cognitive appraisals, and in pain levels. In a sub-group of sufferers with significant levels of trauma symptoms, the index images elicited significantly higher levels of emotion and pain increment than did those respondents in a low/no trauma group. CONCLUSION: It was concluded that imagery is a prevalent, often "unobserved" but potent cognition in pain sufferers. The implications for CBT approaches to chronic pain, including image rescripting, are considered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".