Consensus on Methods to Foster Physical Therapy Professional Behaviors
Bibliographic record
Abstract
Background. An increased awareness of pro-fessionalism and professional behaviors is occurring in many health care disciplines. Key physical therapy professional behaviors have been developed. It is now essential to develop methods to foster professional behaviors within physical therapy students and educators. Purpose. The purpose of this study was to achieve a consensus on methods to foster pro-fessional behaviors among key stakeholders in the School of Physical Therapy at The University of Western Ontario. Method. The Nominal Group Technique and Delphi process were used to develop strategies and methods to foster pro-fessionalbehaviors among students (n=4), clinical instructors (CIs, n=5), and faculty (n=2) using a developmental framework. Results. The key methods identified to foster professional behaviors were: lead by example, explicit teaching, mentoring, reflective imaging, and wider context education. Conclusions and Educational Implications. The resulting techniques and methods to foster professional behaviors provide a common frameivork upon which students, CIs, and faculty might reflect, discuss, and then use to develop professional behaviors. The strategies provide choices to students, Cls, and faculty according to variations in learning environments, learning styles, stages of development of professional behaviors, and individual goals.
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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 | 0.194 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".