A multi-institution longitudinal randomised control trial of speaking up: Implications for theory and practice
Bibliographic record
Abstract
Background: Speaking up is an important yet challenging aspect of health professional communication. To overcome social-cognitive influences and improve speaking up, an intervention based on Kolb's experiential learning cycle was developed, which integrated Virtual Simulation, curriculum, and practice speaking up. The present study investigated if integrating Virtual Simulation influenced Respiratory Therapy students' ability to challenge a physician compared to a control condition at multiple time points during training. Methods: A multi-institutional longitudinal randomized control trial was conducted. Students from two schools completed a Virtual Simulation or No Virtual Simulation before classroom instruction on speaking up and an in-person simulation requiring speaking up. After three-to-six months and post-clinical placement, students completed a second simulation requiring speaking up. The student's ability to speak up and use CUS (Concerned, Uncomfortable, Safety Issue) was measured. Results: , with a small effect for using CUS, ϕ=.28. During the study, two unexpected findings emerged with theoretical and practical implications. The multi-institutional design created a natural experiment that allowed for the identification of instructor effects on speaking up and Bloom's Two-Sigma problem. Observations were also made related to perceptual limitations that diminish the ability to speak up. Conclusions: Single speaking-up interventions continue to appear to be ineffective. To substantially influence behaviour, consistent mentorship through a "champion" is likely necessary to train for and create a culture of speaking up. Training in situational awareness is also likely needed to counter human perceptual limitations in complex situations.
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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 | 0.070 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".