Comparisons of Clinical Competency and Job Responsibilities of Physical Therapists With and Without Postprofessional Training
Bibliographic record
Abstract
INTRODUCTION: Understanding how educational pathways may influence clinical competence and work responsibilities is important in providing guidance to academic and clinic stakeholders and physical therapists (PTs) on PT career development. The purpose of this paper was to compare perceived clinical competency and job duties between PTs with formal mentored postprofessional clinical education with PTs without formal postprofessional clinical education. REVIEW OF LITERATURE: The understanding of self-perceived clinical competence of PTs overall in the United States is limited, especially as related to the impact of postprofessional education. Furthermore, there is limited understanding of the career pathways and development of job duties of PTs in the United States. SUBJECTS: Two thousand three hundred thirty-four PTs in the United States. METHODS: An online survey was sent to licensed PTs. This survey included items measuring self-perceived clinical competency and questions related to weekly job responsibilities. Participants were categorized as residency trained, fellowship trained, or non-residency/fellowship trained. Frequency analyses and Kruskal-Wallis tests with pairwise post hoc tests were performed comparing the 3 groups. RESULTS: Residency-trained (P < .007) and fellowship-trained (P < .001) groups demonstrated elements of higher self-perceived clinical competency compared with the non-residency-/fellowship-trained group. Both the residency- and fellowship-trained groups spent less time with patient care and more time with other responsibilities (e.g., teaching, mentoring, and research) (P < .02) during an average workweek. Fellowship-trained PTs (P < .001) spent more time with administrative duties compared with the non-residency-/fellowship-trained group. DISCUSSION AND CONCLUSION: Results from this paper demonstrate that residency- and fellowship-trained PTs have elements of higher self-perceived clinical competency and spend more of their job duties outside of direct patient care compared with PTs who were not residency or fellowship trained. These results may help guide PTs, academic institutions, and employers in planning and achieving specific career paths.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".