Planning and implementing a collaborative clinical placement for medical, nursing and allied health students: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Clinical placements have been traditionally offered on a profession specific basis, and as a result, we have a good understanding of salient issues related to their development and delivery. We know less about the planning and implementation of collaborative clinical placements. AIMS: This paper presents key findings from a qualitative study that explored the collaborative processes connected to an interprofessional planning group who created and implemented a clinical placement for medical, nursing and allied health students. METHODS: An ethnographic approach was employed to explore the successes and challenges connected with the planning group's interprofessional work. Interviews, observations and documents were gathered over two years to obtain a comprehensive understanding of this placement. RESULTS: The study found that while the planning group achieved a number of successes in their work including the implementation of a well-received pilot placement, their enthusiasm for the placement created a number of challenges. In particular, it resulted in them neglecting their roles, responsibilities and collaborative group processes, which created difficulties in their ability to work together. In addition, a turnover of members, changes in management and a hospital reorganization inhibited the group's collaborative work. CONCLUSIONS: Collaboration around the planning and implementation of interprofessional placements is a complex venture. In striving for success in this work, planning groups need to focus their attention on both internal group-based factors as well external organizational factors.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".