Impact of physical therapy and occupational therapy student placements on productivity: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Clinical educators may perceive that student supervision is time consuming and reduces productivity. This perception is in contrast to research conducted in the 1990's that found students do not negatively impact productivity. There is a need to review the current literature on this topic as a result of health care cost-containment measures that emphasize efficiency. The purpose of this scoping review was to map and examine the impact of physical and occupational therapy student placements on productivity in the clinical environment. METHODS: PRISMA Scoping review methodology was used to identify relevant papers. A search was completed in MEDLINE, CHINAL, ERIC and Business Source Premier. Included studies measured clinician productivity while supervising a physical or occupational therapy student. Two reviewers independently reviewed studies according to pre-determined eligibility criteria. RESULTS AND DISCUSSION: Fourteen studies met the inclusion criteria and were included in the review. Overall, the studies suggest that the supervision of students does not have a negative impact on productivity. However, the productivity measures varied in the type and methods which limits comparisons. This variability, along with the experience of stress by clinical educators as they attempt to satisfy multiple roles may account for the discrepancy between the perception and actual measure of productivity. CONCLUSIONS: This scoping review found some evidence that students do not negatively impact productivity. This contrasts with the perception held by the supervising physical and occupational therapists. Further research is recommended to explore this discrepancy and determine optimal productivity measures matched to the characteristics of the environment.
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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 | 0.021 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".