Subject-generated internal imagery coupled with relaxation as a treatment for chronic pain / Katherine A. Farmer.
Bibliographic record
Abstract
The chronic pain experience is a multifaceted phenomenon \ninvolving sensory, cognitive, affective, motivational and behavioral \ndimensions. There has been no single consistently successful method of \npain control and multiple treatment approaches are frequently utilized by \nthe chronic pain sufferer. The treatment approach investigated in this \nexperiment used a relaxation technique coupled with visualization. \nThirty-two chronic pain subjects with various diagnoses were divided into \nfour groups using a quasi-random design. Two groups received training \nin a relaxation technique for eight weeks, and two groups started with \nrelaxation and then were also given a visualization procedure for the \nfinal four weeks. Assessments using the McGill Pain Questionnaire, the \nMultidimensional Health Locus of Control, the Profile of Mood States, \nand the West Haven-Yale Multidimensional Pain Inventory were done \nbefore treatment, at the mid-point, and at the end of treatment. \nThe results showed no consistent differences between treatment \ngroups and failed to indicate any clear-cut advantages for either \nrelaxation or visualization in controlling chronic pain. There was no \nconsistent reduction in pain or pain behaviors over the course of the \nexperiment regardless of situation.
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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 | 0.000 |
| 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) | 0.006 | 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".