Collaborative Clinical Placements: Interactions Among Students From Different Programs
Bibliographic record
Abstract
BACKGROUND: Shortages of clinical placements for health care students in Canada have led education and health care organizations to explore innovative ways to increase placement capacity. One way to increase capacity is to bring together students from various programs for their placements, which also allows students to learn about each other's roles and how to work collaboratively. This article describes shared placements for students from bachelor of nursing, practical nurse, and health care aide programs. METHOD: Qualitative interviews were used. RESULTS: Students benefited from this approach by learning about the roles of other providers and how to coordinate care with others. The challenges of the approach were competition among students for opportunities to practice clinical procedures and concerns about how to communicate with other students when sharing the care of patients. CONCLUSION: The objectives of increasing student placement capacity and expanding collaboration opportunities were partially achieved through this approach to clinical education.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".