Unraveling pain experience and catastrophizing after cognitive behavioral therapy
Bibliographic record
Abstract
Pain experiences are often complex with catastrophic cognitions, emotions, and behaviors. Cognitive behavioral therapists share the work of unraveling these complex experiences with their patients. However, the change process underlying the unraveling of the pain experience have not yet been quantified. We used an interrelationship-focused network model to examine the way an undifferentiated conceptualization between cognition and pain experience changed via group cognitive-behavioral therapy (CBT). Overall, 65 participants (77.4% of all patients who entered the intervention) were included in the analysis; they attended the total of 12 weekly group CBT and filled the Short-Form McGill Pain Questionnaire and the pain catastrophizing questionnaire. Before treatment, there were no edges in the partial correlation-based network because of large covariation across items. After treatment, many edges appeared and, particularly strong couplings were found between items within the same subscale. The formative shift from a non-edged pre-treatment network to a mature post-treatment network may indicate that patients were able to conceptualize these symbolic constructs better. These results are probably of interest to clinicians and would be consistent with the fundamental monitoring process of CBT.
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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 | 0.008 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".