Use of Simulation in Canadian Neonatal-Perinatal Medicine Training Programs
Bibliographic record
Abstract
Introduction Simulation is used for the delivery of education and on occasion assessment. Before such a tool is used routinely in neonatal training programs across Canada, a need assessment is required to determine its current usage by accredited training programs. Our aim was to characterize the type of simulation modalities used and the perceived simulation-based training needs in Canadian neonatal-perinatal medicine (NPM) training programs. Methods A 22-item and 13-item online descriptive survey was sent to all NPM program directors and fellows in Canada, respectively. The survey was modeled on a previously validated tool by Johnston, et al. and responses were collected over 30 days. Results In total, eight (63%) program directors and 24 (28%) fellows completed the survey, with all respondents indicating that simulation is being used. Both lab-based and in situ simulations are occurring, with a range of simulation modalities employed to primarily teach resuscitation, procedural and communication skills. Fellows indicated that simulation should also be used to also teach other important topics, including disease-specific management, crisis resource management, and prevention of medical error. Five (63%) programs have faculty with formal simulation training and four (50%) programs have at least one faculty involved in simulation research. Conclusion Simulation is widely used in Canadian NPM training programs, with program directors and fellows identifying this as an important tool. Simulation can be used to teach a range of skills, but programs need to align their curriculum with both training objectives and learner needs. There is an opportunity for faculty development and increased simulation research.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".