“Maybe I’m not that approachable”: using simulation to elicit team leaders’ perceptions of their role in facilitating speaking up behaviors
Bibliographic record
Abstract
BACKGROUND: Simulation research that seeks to solve the problem of silence among interprofessional teams has focused almost exclusively on training subordinate team members to be more courageous and to speak up to team leaders using direct challenge scripts despite the great interpersonal cost. Consequently, the existing literature overemphasizes the responsibility of subordinate team members for speaking up and fails to consider the role and responsibilities of team leaders in sustaining silence. The purpose of this study is to identify and describe the subtle behaviors and actions of team leaders that both promote and discourage speaking up. METHODS: This study used a simulation-primed qualitative inquiry approach. Obstetricians (OB) at one academic center participated in an interprofessional simulation as an embedded participant. Five challenge moments (CM) were scripted for the OB involving deliberate clinical judgment errors or professionalism infractions. Other participants were unaware of the OB embedded participant role. Thirteen iterations were completed with 39 participants. Twelve faculty members completed a subsequent semi-structured interview. Scenarios were videotaped; debriefs and interviews were audio-recorded and transcribed verbatim. Data were analyzed using an inductive thematic approach. RESULTS: After participating in an interprofessional simulation, faculty participants reflected that being an approachable team leader requires more than simply avoiding disruptive behaviors. We found that approachability necessitates that team leaders actively create the conditions in which team members perceive that speaking up is welcomed, rather than an act of bravery. In practice, this conceptualization of approachability involves the tangible actions of signaling availability through presence, uncertainty through thinking aloud, and vulnerability through debriefing. CONCLUSIONS: By using faculty as embedded participants with scripted errors, our simulation design provided an ideal learning opportunity to prompt discussion of the subtle behaviors and actions of team leaders that both promote and discourage speaking up. Faculty participants gained a new appreciation that their actions create the conditions for speaking up to occur before critical incidents through their verbal and non-verbal communication.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".