From didactic explanations to co-design, sequential art and embodied learning: challenges, criticisms and future directions of patient pain education
Bibliographic record
Abstract
Pain Neuroscience Education (PNE) emerged over two decades ago in response to the incoherence between evidence-based pain management strategies, and consumer and clinician understandings of "how pain works". Many clinical trials have investigated the effects of PNE either as a standalone intervention or embedded within a more complex care package, with mixed results. A range of research methods have been used to explore the inconsistent effects of PNE. Together they (i) identify significant shortcomings and limitations of PNE and (ii) raise the possibility that gaining a broadly scientifically accurate understanding of "how pain works" may be critical for subsequent pain and disability improvements. Both learnings strongly suggest that we need to do better. Extensive research incorporating several interest-holders has led to updated content and language and criticisms of both are addressed. The method of PNE has also been updated, with integration of educational frameworks, teaching strategies and tactics, patient resources and clinical tools that all aim to promote the likelihood that patients will learn key concepts and operationalise them to improve their pain, function and quality of life. Pain Science Education is used to differentiate the new approach from PNE.
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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 | 0.034 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".