Education about pain and experience with cognitive-based interventions do not reduce healthcare professionals’ chronic pain
Bibliographic record
Abstract
Background: Cognitive-based interventions like pain neuroscience education (PNE), cognitive behavioral therapy (CBT), acceptance commitment therapy (ACT), and mindfulness meditation are popular for managing chronic pain. Despite their widespread adoption, evidence for their efficacy remains contradictory. Healthcare professionals (HCPs) represent a unique population to evaluate these approaches, as they possess specialized knowledge about pain mechanisms and often implement these interventions with patients. The logical premise underlying cognitive-based interventions suggests that increased knowledge and cognitive engagement with pain concepts should reduce pain intensity, making educated HCPs with chronic pain an ideal test case for this theoretical framework. Purpose: To investigate whether HCPs with chronic pain who (HCPs+CP) are familiar with these methods experience less pain and improved quality of life compared to less experienced HCPs+CP and healthy HCPs (H-HCPs). Methods: This cross-sectional study used an anonymous online questionnaire distributed in English through closed professional social media groups internationally. Data were collected from 550 HCPs (319 healthy, 231 with chronic pain) primarily from Israel, Canada, United States, United Kingdom, and Australia. Participants were categorized by their knowledge of pain neuroscience, experience with cognitive-based interventions, and chronic pain type (primary or secondary). Pain intensity was measured using the Numerical Pain Rating Scale, and quality of life was assessed with the World Health Organization tool the WHOQOL-BREF. Statistical analyses included Spearman's correlation tests and independent samples t-tests. Results: > 0.05). Among the 146 H-HCPs who had recovered from chronic pain, only 11% attributed their recovery to cognitive-based interventions, while the majority credited physical therapy (37.7%) and spontaneous recovery (32.9%). Conclusion: Despite their specialized knowledge and experience with cognitive-based interventions, HCPs+CP did not report reduced pain intensity, though they maintained quality of life comparable to healthy colleagues. These findings challenge current theoretical models underlying cognitive-based pain management.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".