Epistemological Flexibility in Person-Centered Care: The Cynefin Framework for (Re)Integrating Indigenous Body Representations in Manual Therapy
Bibliographic record
Abstract
BACKGROUND: Chiropractic, osteopathy, and physiotherapy (COP) professionals regulated outside the United States traditionally incorporate hands-on procedures aligned with their historical principles to guide patient care. However, some authors in COP research advocate a pan-professional, evidence-informed, patient-centered approach to musculoskeletal care, emphasizing hands-off management of patients through education and exercise therapy. The extent to which non-Western sociocultural beliefs about body representations in health and disease, including Indigenous beliefs, could influence the patient-practitioner dyad and affect the interpretation of pillars of evidence-informed practice, such as patient-centered care and patient expectations, remains unknown. METHODS: our perspective paper combines the best available evidence with expert insights and unique viewpoints to address gaps in the scientific literature and inform an interdisciplinary readership. RESULTS: A COP pan-professional approach tends to marginalize approaches, such as prevention-oriented clinical scenarios traditionally advocated by osteopathic practitioners for patients with non-Western sociocultural health assumptions. The Cynefin framework was introduced as a decision-making tool to aid clinicians in managing complex clinical scenarios and promoting evidence-informed, patient-centered, and culturally sensitive care. CONCLUSION: Epistemological flexibility is historically rooted in osteopathic care, due to his Indigenous roots. It is imperative to reintroduce conceptual and operative clinical frameworks that better address contemporary health needs, promote inclusion and equality in healthcare, and enhance the quality of manual therapy services beyond COP's Western-centered perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.153 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".