Item generation and pilot testing of the Comprehensive Professional Behaviours Development Log.
Bibliographic record
Abstract
The purpose of this project was to generate and refine criteria for professional behaviors previously identified to be important for physical therapy practice and to develop and pilot test a new instrument, which we have called the Comprehensive Professional Behaviours Development Log (CPBDL). Items were generated from our previous work, the work of Warren May and his colleagues, a competency profile for entry-level physical therapists, our regulatory code of ethics, and an evaluation of clinical performance. A group of eight people, including recent graduates, clinical instructors and professional practice leaders, and faculty members, refined the items in two iterations using the Delphi process. The CPBDL contains nine key professional behaviors with a range of nine to 23 specific behavioral criteria for individuals to reflect on and to indicate the consistency of performance from a selection of "not at all," "sometimes," and "always" response options. Pilot testing with a group of 42 students in the final year of our entry-to-practice curriculum indicated that the criteria were clear, the measure was feasible to complete in a reasonable time frame, and there were no ceiling or floor effects. We believe that others, including health care educators and practicing professionals, might be interested in adapting the CPBDL in their own settings to enhance the professional behaviors of either students in preparation for entry to practice or clinicians wishing to demonstrate continuing competency to professional regulatory bodies.
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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 | 0.047 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".