The perceived contributions of non-physician team members to residents’ interprofessional education during a critical care rotation
Bibliographic record
Abstract
Background: During rotations, post-graduate medical residents must learn about interprofessional teamwork and collaboration. Our study examined the role of non-physician healthcare team members in such education, from the perspectives of both residents and team members themselves. Methods: This qualitative study took place in the intensive care unit (ICU) of a teaching hospital in a Canadian city. We conducted semi-structured individual and focus group interviews with both residents (n = 6) and the team members with whom they collaborated: pharmacists, nurses, respiratory therapists, and a social worker (n = 19). Results: We developed a number of themes about interprofessional education (IPE) in this context from the data, including the presence of planned, unplanned, and tacit teaching; the influence of contextual factors like ICU culture, work demands, resident motivation, power hierarchies, and perceptions of ‘good’ and ‘bad’ residents; the gap between team member perceptions of their contribution to residents’ IP education and residents’ own perceptions; and concerns about the transferability of IPE to other contexts. Conclusions: The influence of non-physician team members on residents’ IPE in the clinical environment is an understudied topic. While our study was limited to one ICU, the themes that emerged may be of interest to others in similar contexts.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".