Updating the Comprehensive Professional Behaviours Development Log.
Bibliographic record
Abstract
PURPOSE: The Comprehensive Professional Behaviours Development Log (CPBDL) was designed as an explicit self-assessment tool to explore developing professional behaviours in entry-level master's of physical therapy students. The purpose of this project was to update the CPBDL to reflect current terminology and practice, using similar stakeholder involvement and consensus processes to those used in its initial development. METHODS: Nine individuals representing a range of stakeholder groups participated in two separate face-to-face meetings. The meetings followed the nominal group technique (NGT). The ideas derived from the NGT meetings were refined via the Delphi process until 80% consensus was reached. RESULTS: Eight of the original nine key professional behaviours were updated; one was deleted (Lifelong Learning). Many items within individual behaviours were merged or re-ordered. Items deemed to be obsolete were removed and new ideas were either added separately or incorporated into previously-written items. Some items were thought to belong better to different behaviours, so these were moved accordingly. Terminology was updated for items and for behaviour titles. DISCUSSION: The nine stakeholders involved in the updating process were satisfied with the new version of the CPBDL. The update better reflects current practices and can be adapted to settings outside physical therapy.
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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 | 0.037 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".