Towards safer interprofessional communication: Constructing a model of “utility” from preoperative team briefings
Bibliographic record
Abstract
"Improved team communication" is broadly advocated in the discourse on safety but rarely supported by a precise understanding of the relationship between specific communication practices and concrete improvements in collaborative work processes. We sought to improve such understanding by analyzing the discourse arising from structured preoperative team briefings among surgeons, nurses, and anesthesiologists prior to general surgery procedures. Analysis of observers' fieldnotes from 302 briefings yielded a two-part model of communicative "utility", defined as the visible impact of communication on team awareness and behavior. "Informational utility" occurred when team awareness or knowledge was improved by provision of new information, explicit confirmation, reminders, or education. "Functional utility" represented direct communication - work connections: many briefings identified problems, prompting decision-making and follow-up actions. The crux of the model is an elaboration of the causal pathway between a specific communication practice (the team briefing), intermediary processes such as enhanced knowledge and purposeful action, and the quality and safety of collaborative care processes. Modeling this pathway is a critical step in promoting change, as it renders visible both the latent dangers present in current team communication systems and the specific ways in which altered communication patterns can impact team awareness and behaviors.
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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 | 0.017 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".