Person-centred care in chiropractic: a foundational but evolving commitment in contemporary practice.
Bibliographic record
Abstract
Background: Person-centred care (PCC) is widely recognized as a cornerstone of high-quality healthcare, linked to improved outcomes and stronger therapeutic relationships. Its core principles of respect, empowerment, and responsiveness to individual needs, are closely aligned with core elements of the chiropractic approach. Yet, translating PCC into consistent practice remains a challenge. Discussion: This commentary explores the value and complexity of PCC in chiropractic, examining barriers such as time constraints, training gaps, patient expectations, and inadequate systemic supports. Conclusion: This paper argues that while chiropractic is well-positioned to embrace PCC, doing so requires a shift from viewing PCC as an inherent feature of the profession to embracing it as an intentional, ethical, and relational commitment. Strategies for advancing PCC are discussed across clinician, patient, and organizational levels to support its consistent and equitable implementation. Author’s Note: This paper is one of seven in a series exploring contemporary perspectives on the application of the evidence-based framework in chiropractic care. The Evidence Based Chiropractic Care (EBCC) initiative aims to support chiropractors in their delivery of optimal patient-centred care. We encourage readers to review all papers in the series.
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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 | 0.057 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.014 | 0.023 |
| 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".