'In situ simulation' versus 'off site simulation' in obstetric emergencies and their effect on knowledge, safety attitudes, team performance, stress, and motivation: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Unexpected obstetric emergencies threaten the safety of pregnant women. As emergencies are rare, they are difficult to learn. Therefore, simulation-based medical education (SBME) seems relevant. In non-systematic reviews on SBME, medical simulation has been suggested to be associated with improved learner outcomes. However, many questions on how SBME can be optimized remain unanswered. One unresolved issue is how 'in situ simulation' (ISS) versus 'off site simulation' (OSS) impact learning. ISS means simulation-based training in the actual patient care unit (in other words, the labor room and operating room). OSS means training in facilities away from the actual patient care unit, either at a simulation centre or in hospital rooms that have been set up for this purpose. METHODS AND DESIGN: The objective of this randomized trial is to study the effect of ISS versus OSS on individual learning outcome, safety attitude, motivation, stress, and team performance amongst multi-professional obstetric-anesthesia teams.The trial is a single-centre randomized superiority trial including 100 participants. The inclusion criteria were health-care professionals employed at the department of obstetrics or anesthesia at Rigshospitalet, Copenhagen, who were working on shifts and gave written informed consent. Exclusion criteria were managers with staff responsibilities, and staff who were actively taking part in preparation of the trial. The same obstetric multi-professional training was conducted in the two simulation settings. The experimental group was exposed to training in the ISS setting, and the control group in the OSS setting. The primary outcome is the individual score on a knowledge test. Exploratory outcomes are individual scores on a safety attitudes questionnaire, a stress inventory, salivary cortisol levels, an intrinsic motivation inventory, results from a questionnaire evaluating perceptions of the simulation and suggested changes needed in the organization, a team-based score on video-assessed team performance and on selected clinical performance. DISCUSSION: The perspective is to provide new knowledge on contextual effects of different simulation settings. TRIAL REGISTRATION: ClincialTrials.gov NCT01792674.
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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 | 0.036 | 0.043 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
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".