Interprofessional Communication and Medical Error: A Reframing of Research Questions and Approaches
Bibliographic record
Abstract
Progress toward understanding the links between interprofessional communication and issues of medical error has been slow. Recent research proposes that this delay may result from overlooking the complexities involved in interprofessional care. Medical education initiatives in this domain tend to simplify the complexities of team membership fluidity, rotation, and use of communication tools. A new theoretically informed research approach is required to take into account these complexities. To generate such an approach, we review two theories from the social sciences: Activity Theory and Knotworking. Using these perspectives, we propose that research into interprofessional communication and medical error can develop better understandings of (1) how and why medical errors are generated and (2) how and why gaps in team defenses occur. Such complexities will have to be investigated if students and practicing clinicians are to be adequately prepared to work safely in interprofessional teams.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".