Current Practices and Use of Simulation in Neonatal Resuscitation Program Courses Across Canada
Bibliographic record
Abstract
Abstract BACKGROUND: Simulation is an effective tool in medical education. The extent and manner in which simulation is used within Neonatal Resuscitation Program (NRP) courses across Canada is currently unknown. In order to improve NRP education, current practices must be better understood. OBJECTIVES: To characterize current practices and use of simulation in NRP courses across Canada. DESIGN/METHODS: A REDCap survey, consisting of questions about instructor demographics, practices in NRP instruction and use of simulation, was developed and distributed to all NRP instructors across Canada. Simple statistics were used to tabulate responses and the chi-squared test was used to assess differences in simulation use between different types of instructors. RESULTS: Five hundred sixty nine of 1390 (40.9%) NRP instructors completed the survey. Participants included 88 (15.5%) physicians, 74 (13.0%) respiratory therapists, 345 (60.6%) registered nurses and 28 (4.9%) nurse practitioners. Two hundred fifty eight (45.4%) worked in institutions providing Level III care. Overall, 560 (98.4%) respondents used simulation, of which only 176 (31.4%) reported using high-technology simulation. Only 180 (31.6%) instructors who used simulation reported having received formal training in high-technology simulation. When asked about the role of simulation in NRP instruction, 545 (95.8%) agreed or strongly agreed that simulation is a valuable educational tool in NRP instruction, but only 219 (39.1%) felt comfortable using high-technology simulation. There was no difference in use of high-technology simulation between physician and non-physician instructors (I2 0.90, p=0.34). Of the instructors who used high-technology simulation, 160 (90.9%) and 134 (76.1%) had learners and instructors, respectively, from multiple healthcare disciplines present in some or all sessions. There was a non-significant trend towards higher use of interprofessional learners among physician instructors (I2 3.8, p=0.052). An impressive 554 (98.9%) debriefed after some or all simulation sessions, with only 295 (51.8%) instructors having received formal training in debriefing techniques. CONCLUSION: Almost all NRP instructors use simulation and feel that it is valuable, though few have received formal training and feel comfortable using high-technology simulation. Most simulation use is low-technology, in keeping with the Canadian Paediatric Society (CPS) recommendations, though the optimal methods of use of simulation in NRP instruction are not known. The majority of instructors debrief with learners, as recommended by the CPS, though only half have had training in debriefing. The results of this study support further investigation into the optimal type of simulation in NRP teaching and more formal education in simulation and debriefing for NRP instructors.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".