A low resource simulation-based training package leads to increased knowledge and skill retention post Basic Emergency Obstetric and Neonatal Care in rural Tanzania: A Quasi-experimental research design
Bibliographic record
Abstract
Abstract Background: Neonatal and Maternal mortality remain alarmingly high in rural areas like Kwimba Tanzania. The Basic Emergency Obstetric and Neonatal Care (BEmONC) training program aims to decrease these rates by improving healthcare provider knowledge and skill. Literature supports improvement in knowledge and skill immediately after training, but skill decay is reported as early as 1 month if healthcare providers are not afforded further facility-based opportunities to practice. The purpose of this study was to examine a low-cost resource option, a simulation package, for its impact on retention of knowledge and skills over a one-year period. Methods: A quasi-experimental research design was used to test the intervention; 5-day BEmONC training plus a facility-based simulation package: (1) low dose high frequency peer to peer simulation practice using peer cards and (3) clinical mentorship. Participants were midwives/nurses, clinical officers, and medical officers from local hospitals and health centers in Kwimba, Tanzania. Facilities were purposely assigned to one of the two clusters. After initial BEmONC training. Cluster A was supported with the simulation package whereas access to the simulation package was delayed until after the 6-month assessment for Cluster B. Knowledge and skill were analyzed using the training program OSCE’s at baseline, post workshop, at 6 months and at 12 months using the r core statistics; p-values < 0.05 were considered statistically significant. Results: All participants demonstrated significant knowledge and skill improvements post-initial workshop. At 6 months, Cluster A’s aggregate skill scores were significantly higher than Cluster B, who showed skill decay. At 12 months, aggregate skill scores between Cluster A and Cluster B were similar. Conclusion: There was a significant relationship between clusters receiving component two and three of the interventions and retention of skill sets at 6 and 12 months. This is the first study to report skill retention at 12 months after BEmONC training. Peer learning using detailed peer learning cards, with mentorship visits by the clinical expert every 3 months is a low resource educational option that in this context supported skill retention. More research is needed to assess generalizability and link like initiatives with clinical outcomes.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".