Mono-Professional Simulation-Based Obstetric Training in a Low-Resource Setting: Stepped-Wedge Cluster Randomized Trial
Bibliographic record
Abstract
Background: Emergency obstetric simulation-based training has increasingly been used to improve emergency obstetric care provision in sub-Saharan Africa. For determining the optimal methodology for effective training sessions in resource-constrained settings, it is crucial to conduct high-quality research. Objective: We aim to investigate the impact of a train-the-trainer model for providing technology-enhanced, mono-professional, simulation-based training in obstetrics in a resource-constrained setting on maternal and perinatal outcomes. Methods: A stepped-wedge cluster randomized trial was conducted from October 2014 until March 2016 at the medium- to high-risk ward at Mulago National Referral Hospital, Uganda, with an annual delivery rate of over 23,000. The intervention consisted of a train-the-trainer model in which training was cascaded down from master trainers to local facilitators (obstetric senior staff members) to learners (senior house officers). The training of senior house officers was provided to 7 fixed clusters by a computer-generated random sequential roll-out. The training comprised a 1-day (8 h), mono-professional, simulation-based training in obstetrics, and half-day repetition training sessions targeted at every 7 weeks. Both medical technical skills and teamwork skills were taught. The primary outcome comprised a combined maternal and perinatal mortality rate. Secondary outcomes comprised the maternal mortality rate, the perinatal mortality rate, the percentage of births by vacuum extraction and cesarean section, and the Weighted Adverse Outcome Score. Results: Overall, there were 17,496 births. The combined mortality rate was 9.05% (95% CI 8.37%-9.77%) in the intervention group, and 8.73% (95% CI 8.21%-9.28%) in the control group (odds ratio [OR] 0.98, 95% CI 0.86-1.12; P=.81). No statistically significant change was found in the maternal mortality rate (OR 0.80, 95% CI 0.27-2.32; P=.68) or the perinatal mortality rate (OR 0.99, 95% CI 0.87-1.13; P=.87). This study did not identify any difference in the percentage of vacuum extractions, the percentage of cesarean sections, or Weighted Adverse Outcome Scores. Conclusions: This train-the-trainer model for providing technology-enhanced, mono-professional, simulation-based training in obstetrics was not able to change maternal and perinatal mortality outcomes. This study, in combination with literature, suggests that future research should consider multiprofessional team training in obstetrics involving all staff within their units.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".