Development, Implementation and Evaluation of an Acute Care Physical Therapy ‘Float’ Placement during the COVID-19 Pandemic: A Case Report
Bibliographic record
Abstract
Clinical education is a mandatory component of physical therapy curricula globally. COVID-19 disrupted clinical education, jeopardizing students' abilities to meet graduation requirements. The objective of this case report is to outline the development, implementation and evaluation of a multiple clinical instructor (CI), multiple unit, acute care float clinical placement for a final year, entry-level physical therapy student and offer implementation recommendations. This placement included an eight-week, multiple CI (one primary, four supporting), multiple (five) unit clinical placement which was developed between St. Joseph's Healthcare and the McMaster University Masters of Science (Physiotherapy) Program between 10 August and 2 October 2020. Student evaluations and reflections by the student and CIs were collected and analyzed using interpretive description. Analysis from the reflections revealed six themes: (1) CI and student attributes; (2) increased feasibility; (3) varied exposure; (4) central communication and resources; (5) organization; and (6) managing expectations. An acute care clinical experience is required for students in Canadian entry-to-practice physical therapy programs. Due to COVID-19, placement opportunities were limited. The float placement allowed clinicians to offer supervision despite staff re-deployment and increased organizational and work-life pressures during the pandemic. This model provides an approach to extenuating circumstances and may also increase acute care placements during non-pandemic times for physical therapy and other similarly structured healthcare professions.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".