Self‐Critical Perfectionism Predicts Outcome in Multidisciplinary Treatment for Chronic Pain
Bibliographic record
Abstract
BACKGROUND: Self-critical perfectionistic personality features have been shown to influence the onset and perpetuation of pain symptoms. However, no study to date has investigated whether these personality features are associated with treatment response in chronic pain. METHODS: Using a naturalistic pre-post design, the present study examined the effect of self-critical perfectionism on treatment outcome in terms of self-reported pain. The study was conducted in a sample of 53 chronic non-cancer pain patients who followed Multidisciplinary Pain Education Program (MPEP), a brief, 2-week cognitive-behaviorally based psycho-educational intervention for chronic pain that was recently found to be effective in reducing pain severity. Pre- and post-treatment pain intensity levels were assessed with the visual analog scale of the McGill Pain Questionnaire-Short Form. RESULTS: Pretreatment self-critical perfectionism was significantly associated with negative treatment outcome, even after taking into account pretreatment levels of depression. CONCLUSION: Results suggest that self-critical perfectionistic personality features may negatively interfere with treatment response in patients with chronic pain. Thus, findings indicate that chronic pain patients with high levels of self-critical perfectionism may benefit less from brief interventions such as MPEP, and therefore may need more intensive and tailored treatment.
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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 | 0.006 |
| 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 | 0.001 |
| 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".