A review of the evidence for the maximization of clinical placement opportunities through interprofessional collaboration
Bibliographic record
Abstract
Interprofessional collaboration has been suggested as a potential solution to clinical placement shortages. This review was designed to compile the evidence on the use of interprofessional and nursing intraprofessional collaboration to maximize clinical placement opportunities for undergraduate/pre-licensure health professional students. A worldwide search of the published and grey literature was conducted, supplemented by 28 interviews across Canada. Results revealed only two articles, both in nursing – one that described an intraprofessional nursing collaboration consisting of a clinical placement consortium for finding placements, and one that described a unique program that provided a seamless transition from a practice nurse program to associate degree nursing program and that included collaboration with clinical placements. The interviews revealed various types of collaborations designed to maximize placements and various mechanisms by which these collaborations served to maximize placements including easing the pressure to find preceptors and increasing the number of placements. The authors concluded that while collaborations exist specifically to maximize placements, at least in Canada, it was neither happening within the context of research nor being formally evaluated. More evaluation is needed in order to clarify the evidence by which collaboration works or does not work to maximize placement opportunities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".