The experiences of administrators, educators and clinicians during the development and implementation of interprofessional clinical learning units
Bibliographic record
Abstract
Introduction: Interprofessional and collaborative practice is essential for effective patient care in new and evolving healthcare service delivery models. Traditionally, interprofessional clinical learning has focused on students and clinicians, however healthcare administrators and managers may play a key role in the success of interprofessional clinical learning. In this paper, the triumphs and trials of those engaged in the interprofessional clinical learning unit (IPCLU) conceptualisation, development and implementation are presented.Methods: Over 60 executives, directors, frontline managers, educators, researchers and staff participated in the development and initiation of an IPLCU in three distinct clinical settings in Alberta: tertiary rehabilitation, acute care and continuing care. Focus groups were used to explore participants’ experiences of developing, initiating and implementing an IPLCU.Results: A qualitative analysis revealed the following predominant themes that describe significant outcomes or considerations: pre-IPCLU challenges, team dynamics, student experiences, cultural changes, sustainability and leadership.Conclusions: Successful implementation of IPCLUs can be achieved with participation and leadership from clinicians and educators and the support of administration at both the academic institution and the healthcare agency.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".