Every team needs a coach: Training for interprofessional clinical placements
Bibliographic record
Abstract
Despite growing awareness of the benefits of interprofessional education and interprofessional collaboration (IPC), understanding how teams successfully transition to IPC is limited. Student exposure to interprofessional teams fosters the learners' integration and application of classroom-based interprofessional theory to practice. A further benefit might be reinforcing the value of IPC to members of the mentoring team and strengthening their IPC. The research question for this study was: Does training in IPC and clinical team facilitation and mentorship of pre-licensure learners during interprofessional clinical placements improve the mentoring teams' collaborative working relationships compared to control teams? Statistical analyses included repeated time analysis multivariate analysis of variance (MANOVA). Teams on four clinical units participated in the project. Impact on intervention teams pre- versus post-interprofessional clinical placement was modest with only the Cost of Team score of the Attitudes Towards Healthcare Team Scale improving relative to controls (p = 0.059) although reflective evaluations by intervention team members noted many perceived benefits of interprofessional clinical placements. The significantly higher group scores for control teams (geriatric and palliative care) on three of four subscales of the Assessment of Interprofessional Team Collaboration Scale underscore our need to better understand the unique features within geriatric and palliative care settings that foster superior IPC and to recognise that the transition to IPC likely requires a more diverse intervention than the interprofessional clinical placement experience implemented in this study. More recently, it is encouraging to see the development of innovative tools that use an evidence-based, multi-dimensional approach to support teams in their transition to IPC.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".