“They put you on your toes”: Physical Therapists' Perceived Benefits from and Barriers to Supervising Students in the Clinical Setting
Bibliographic record
Abstract
PURPOSE: To identify the perceived benefits of and barriers to clinical supervision of physical therapy (PT) students. METHOD: In this qualitative descriptive study, three focus groups and six key-informant interviews were conducted with clinical physical therapists or administrators working in acute care, orthopaedic rehabilitation, or complex continuing care. Data were coded and analyzed for common ideas using a constant comparison approach. RESULTS: Perceived barriers to supervising students tended to be extrinsic: time and space constraints, challenging or difficult students, and decreased autonomy or flexibility for the clinical physical therapists. Benefits tended to be intrinsic: teaching provided personal gratification by promoting reflective practice and exposing clinical educators to current knowledge. The culture of different health care institutions was an important factor in therapists' perceptions of student supervision. CONCLUSIONS: Despite different disciplines and models of supervision, there is considerable synchronicity in the issues reported by physical therapists and other disciplines. Embedding the value of clinical teaching in the institution, along with strong communication links among academic partners, institutions, and potential clinical faculty, may mitigate barriers and increase the commitment and satisfaction of teaching staff.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".