Avoiding nocebo and other undesirable effects in chiropractic, osteopathy and physiotherapy: An invitation to reflect
Bibliographic record
Abstract
INTRODUCTION: While the placebo effect is increasingly recognised as a contributor to treatment effects in clinical practice, the nocebo and other undesirable effects are less well explored and likely underestimated. In the chiropractic, osteopathy and physiotherapy professions, some aspects of historical models of care may arguably increase the risk of nocebo effects. PURPOSE: In this masterclass article, clinicians, researchers, and educators are invited to reflect on such possibilities, in an attempt to stimulate research and raise awareness for the mitigation of such undesirable effects. IMPLICATIONS: This masterclass briefly introduces the nocebo effect and its underlying mechanisms. It then traces the historical development of chiropractic, osteopathy, and physiotherapy, arguing that there was and continues to be an excessive focus on the patient's body. Next, aspects of clinical practice, including communication, the therapeutic relationship, clinical rituals, and the wider social and economic context of practice are examined for their potential to generate nocebo and other undesirable effects. To aid reflection, a model to reflect on clinical practice and individual professions through the 'prism' of nocebo and other undesirable effects is introduced and illustrated. Finally, steps are proposed for how researchers, educators, and practitioners can maximise positive and minimise negative clinical context.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".