Pain Catastrophizing and Fear of Movement: Detection and Intervention
Bibliographic record
Abstract
Chronic pain is one of the most common reasons for the use of complementary and alternative medicine (CAM). There are a great many such treatments available, almost all of which are paid for directly by the patient, and for which there may be little or no scientific rationale or clinical evidence of safety and efficacy. In this chapter, a framework for addressing the issue of CAM use in the context of chronic pain management is outlined. Included in the chapter are examples of questions to use in bringing up the subject of CAM use in the clinical interview, reasons why patients use CAM, tips for how to better understand the patient through their self-reported use of CAM, and guidance on how to define and categorize CAM treatments. Some of the more well-studied CAM modalities are listed and suggestions for approaching issues of quality (through regulation of products and practices) are addressed. Attitudes towards CAM use by healthcare professionals are discussed, and means by which discussion of CAM use may enhance the practitioner–patient relationship 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".