Bibliographic record
Abstract
BACKGROUND: The majority of pain sufferers experience images when in pain. The most distressing of these images (the Index image) provokes intense emotional reactions, appraisal shifts, and increases in pain. The ability of pain sufferers to rescript their Index images, and the consequences of doing so, remain to be determined. AIMS: To assess the effects upon emotions, appraisals and pain experience of rescripting Index images in pain sufferers. METHOD: The Index images of a group of 55 pain sufferers were assessed using a voluntary image induction procedure (VIE) to obtain basal levels of pain, appraisal and emotion. Participants were than randomly allocated to one of two groups: Rescripted Image repetition or Index Image repetition. The two groups were compared on their responses to their Index and Rescripted images respectively. RESULTS: The participants found it easy to rescript their distressing Index images. During rescripting, they reported dramatic reductions in emotion, negative appraisals, and pain. The clinically and statistically significant decrements in pain were found independent of reductions in emotion. The pain levels during rescripting were significantly below their basal levels, with 49% reporting no pain at all while viewing a rescripted image. These changes were not a function of image repetition. CONCLUSION: Index images of pain sufferers can be easily elicited and rescripted. Rescripting leads to remarkable reductions in emotion, cognitions and pain levels that are not attributable to image repetition. The significant reductions in pain were independent of reductions in emotion. The implications of these results for CBT approaches to pain management 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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".