Establishing assessment criteria for clinical reasoning in orthopedic manual physical therapy: a consensus-building study
Bibliographic record
Abstract
OBJECTIVES: Clinical reasoning (CR) represents one of the core components of clinical competence in Orthopaedic Manual Physical Therapy (OMPT). While education standards have been developed to guide curricular design, assessment of CR has not yet been standardized. Without theory-informed and rigorously developed measures, the certification of OMPTs lacks credibility and is less defensible. The purpose of this study was to use a theory-informed approach to generate assessment criteria for developing new assessment tools to evaluate CR in OMPT. METHODS: A list of assessment criteria was generated based on international education standards and multiple theoretical perspectives. A modified Delphi method was used to gain expert consensus on the importance of these assessment criteria for the assessment of CR in OMPT. The OMPTs from 22 countries with experience in assessing CR were invited to participate in three rounds of online questionnaires to rate their level of agreement with these criteria. Responses were tabulated to analyze degree of consensus and internal consistency. RESULTS: Representatives from almost half of the OMPT member organizations (MO) participated in three rounds of the Delphi. High levels of agreement were found among respondents regarding the importance and feasibility of most assessment criteria. There was high internal consistency among items within the proposed item subgroupings. DISCUSSION: A list of assessment criteria has been established that will serve as a framework for developing new assessment tools for CR assessment in OMPT. These criteria will be important for guiding the design of certification processes in OMPT as well as other episodes of CR assessment throughout OMPT training.
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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 | 0.322 | 0.436 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".