“The patient is awake and we need to stay calm”: reconsidering indirect communication in the face of medical error and professionalism lapses
Bibliographic record
Abstract
BACKGROUND: Although speaking up is lauded as a critical patient safety strategy, it remains exceptionally challenging for team members to enact. Existing efforts to address the problem of silence among interprofessional teams involve training low-authority members to use direct language and unambiguous challenge scripts. The role or value of indirect communication in preventing medical error remains largely unexplored despite its pervasiveness among interprofessional teams. This study explores the role of indirect challenges in the face of medical error and professionalism lapses. METHODS: Obstetricians at one academic center participated in an interprofessional simulation as a partial actor. Thirteen iterations were completed with 39 participants (13 obstetrician consultants, 11 obstetric residents, 2 family medicine consultants, 5 midwives, and 8 obstetrical nurses). Thirty participants completed a subsequent semi-structured interview. Five challenge moments were scripted for the obstetrician involving deliberate clinical judgment errors or professionalism infractions. Other participants were unaware of the obstetrician's partial actor role. Scenarios were videotaped; debriefs and interviews were audio-recorded and transcribed verbatim and analyzed using a constructivist qualitative approach. RESULTS: Low-authority team members primarily relied on indirect challenge scripts to promote patient safety during simulation. Faculty participants were highly receptive to indirect challenges from low-authority team members, particularly in front of awake patients. In the context of obstetric care, direct challenges were actually viewed by participants as threatening to patient trust and disruptive to the interprofessional team. Instead of exclusively focusing our efforts on encouraging low-authority team members to speak up through direct challenges, it may be fruitful to expand our attention toward teaching faculty to identify, listen for, and respond to the indirect, subtle challenges that are already prolific among 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 | 0.024 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".