Reconciling movement and exercise with pain neuroscience education: A case for consistent education
Bibliographic record
Abstract
This article will introduce a conceptual framework of kinesthetic education that is consistent with and reinforces pain neuroscience education. This article will also provide some specific guidance for integrating pain neuroscience education with exercise and movement in a more congruent manner. Our belief is that this will enhance the effectiveness of specific movement approaches such as graded exposure techniques. Over the past decade, a new paradigm of pain education has been explored in an effort to improve patient outcomes. Using advances in pain neuroscience, patients are educated in the biological and physiological processes involved in their pain experience. Growing evidence supports the ability of pain neuroscience education (PNE) to positively impact a person's pain ratings, disability, pain catastrophization, and movement limitations. What is often overlooked, however, is the consistency between the messages of PNE and those of other therapeutic interventions, including movement therapies. This article proposes the following: education provided in isolation will be limited in its impact, the addition of guided purposeful movement performed in a manner consistent with PNE may be vital to the desired behavioral changes, and when inconsistent messages are delivered between education and movement interventions, outcomes may be adversely impacted.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.012 |
| 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".