Development, Implementation, and Outcomes of an Acute Care Clinician Scientist Clinical Placement: Case Report
Bibliographic record
Abstract
Purpose: This article presents the development, implementation, and outcomes of an innovative clinician scientist (CS) placement for a 2nd-year, entry-level MSc(PT) student at McMaster University. Client Description: All physiotherapy students participating in the third 6-week clinical placement at McMaster University were eligible to apply for one CS placement. A placement description and expectations were developed collaboratively by the clinical site and the MSc(PT) programme before placement matching. Intervention: A shared supervisory model between one acute care physiotherapist and a critical care CS was developed to provide supervision in both clinical and research-related activities during the placement. Measures and Outcomes: The first CS clinical placement in the MSc(PT) Program at McMaster was completed between November and December 2015. The student was evaluated using the same process as a traditional student placement. Over 6 weeks, the student gained clinical experience in an acute care setting; accumulated more than 100 cardiorespiratory hours; participated in research activities for a randomized controlled trial, which led to a submission to Physiotherapy Practice; and applied for the Canadian Institutes of Health Research Health Professional Student Research Award. Implications: The CS is a developing role for Canadian physiotherapists. A CS placement gave the physiotherapy student the opportunity to apply traditional skills and knowledge as well as to develop advanced research skills. The success of this placement has established a foundation for future placements.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".