A multiple-trainee, multiple-level, multiple-competency (multi-TLC) simulation-based approach to training obstetrical emergencies
Bibliographic record
Abstract
Competency-based education requires that programs increase the breadth of direct observation and assessment to improve resident training. To achieve these goals, the authors developed and executed a multiple-trainee, multiple-level, multiple-competency (Multi-TLC) obstetrical emergencies simulation curriculum. Depending upon their training level (PGY1-PGY5), obstetrics and gynaecology residents participated in various roles (i.e., first responder, second responder, confederates, and evaluators) within four simulation scenarios designed to provide opportunities for education, direct observation, and assessment across a number of competencies (i.e., medical expert, communicator, collaborator, leader, advocate, and scholar). The curriculum was carried out over 8 h spread evenly across 2 days (i.e., 4 h/day) and involved periods of pre-briefing, live simulation, and debriefing. An evaluation of the Multi-TLC was operationalised via a context-input-process-product model. This report presents the outcomes of that evaluation derived from quasi-experimental comparisons of the new and previous curricula across four priorities for simulation-based education identified by the Department of Obstetrics and Gynecology at McMaster University (Hamilton, ON, Canada): increasing learning opportunities, maintaining or improving resident learning, maintaining or reducing program costs, and improving resident satisfaction. The evaluation revealed that the Multi-TLC curriculum permitted a greater breadth of direct observation and assessment across competencies, maintained the previous learning objectives while also addressing additional ones, and was done so in a way that reduced the overall financial and human resource costs associated with the department's obstetrical emergency simulation curriculum. A Multi-TLC organisation of simulation curricula can facilitate efficient application of competency-based education principles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".