Basic emergency Obstetric and Neonatal Care Knowledge retention and Skills of Health Professionals in Burundi following an ALARM International Program Training: A pilot study
Bibliographic record
Abstract
ABSTRACT Background This study describes the immediate and short-term improvement in basic emergency obstetric and neonatal care (BEmONC) knowledge and skills after ALARM International Programme (AIP) training in Burundi. Subjects and Method In 2017, sixteen health professionals participated in a 5-day AIP training. They completed pre and post course tests. At the end of the training, they did objective structured clinical examinations (OSCE) on postpartum hemorrhage (PPH), shoulder dystocia and neonatal resuscitation. In 2018, a refresher course was offered to participants who scored the highest in the 2017 post-training test. Pre and post tests were administered. Mean pre- and post-test scores and t-paired test was performed to determine the knowledge change and retention between 2017 and 2018. Mean OSCE scores were calculated to describe acquired clinical skills at the 2017 training. A one-way ANOVA test was performed to assess the differences between cadres’ scores in the tests and the OSCEs. Results In 2017, pre and post scores were significantly different within cadres except imidwives: physicians t(5)=8.77(p<0.05), midwives t(1)=5,29 (p=0,11) and nurses t(7)=7,91(p<0.05) In 2018, the mean score was 67% (± 10.31) on the pre-test and was 82% (± 5.78) in 2017 post-test. The difference was significant (t(7)=4.80, p<0.05). Scores were significantly different between cadres in PPH [F=(2,13)=6.17, p <0.05)] and dystocia [F=(2,13)=3.92, p <0.05)]. Conclusion An immediate gain in knowledge and acquisition of critical skills was observed in all cadres. Knowledge significantly decreased eight months following AIP training. Supervision, mentoring of trainees and continuing medical education are needed following the initial training.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".