Exploring the teaching and learning of clinical reasoning, risks, and benefits of cervical spine manipulation
Bibliographic record
Abstract
The aim of this study was to examine how risks and benefits of cervical spine manipulation (CSM) were framed and discussed in the context of mentorship and their impact on the perception of safe practice of CSM in clinical physiotherapy settings. A multi-method qualitative approach was employed, including a document analysis of established educational guidelines, observations of mentoring sessions, and individual face-to-face interviews with five mentees in the process of learning CSM, and four mentors with Orthopedic Manual Physical Therapy (OMPT) certification. Results demonstrated that participants' clinical decision-making processes to perform CSM were primarily oriented to the mitigation of risk. Achieving proficiency in the "science" of clinical reasoning and the "art" of "feel" related to mastering technical skills were viewed as means to mitigating risk and enhancing confidence to use CSM safely in clinical practice. While the "art" of technical skill mastery was of high importance to mentees and considered important to developing competency in performing CSM, it was discussed as distinct from their clinical reasoning processes. Thus, promoting a more balanced and integrated use of the "art" and "science" of safe practice for CSM in OMPT training may result in greater confidence and judicious use of CSM by physiotherapists.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".