Correlates of Performance of Healthcare Workers in Emergency, Triage, Assessment and Treatment plus Admission Care (ETAT+) Course in Rwanda: Context Matters
Bibliographic record
Abstract
BACKGROUND: The Emergency, Triage, Assessment and Treatment plus Admission care (ETAT+) course, a comprehensive advanced pediatric life support course, was introduced in Rwanda in 2010 to facilitate the achievement of the fourth Millennium Development Goal. The impact of the course on improving healthcare workers (HCWs) knowledge and practical skills related to providing emergency care to severely ill newborns and children in Rwanda has not been studied. OBJECTIVE: To evaluate the impact of the ETAT+ course on HCWs knowledge and practical skills, and to identify factors associated with greater improvement in knowledge and skills. METHODS: We used a one group, pre-post test study using data collected during ETAT+ course implementation from 2010 to 2013. The paired t-test was used to assess the effect of ETAT+ course on knowledge improvement in participating HCWs. Mixed effects linear and logistic regression models were fitted to explore factors associated with HCWs performance in ETAT+ course knowledge and practical skills assessments, while accounting for clustering of HCWs in hospitals. RESULTS: 374 HCWs were included in the analysis. On average, knowledge scores improved by 22.8/100 (95% confidence interval (CI) 20.5, 25.1). In adjusted models, bilingual (French & English) participants had a greater improvement in knowledge 7.3 (95% CI 4.3, 10.2) and higher odds of passing the practical skills assessment (adjusted odds ratio (aOR) = 2.60; 95% CI 1.25, 5.40) than those who were solely proficient in French. Participants who attended a course outside of their health facility had higher odds of passing the skills assessment (aOR = 2.11; 95% CI 1.01, 4.44) than those who attended one within their health facility. CONCLUSIONS: The current study shows a positive impact of ETAT+ course on improving participants' knowledge and skills related to managing emergency pediatric and neonatal care conditions. The findings regarding key factors influencing ETAT+ course outcomes demonstrate the importance of considering key contextual factors (e.g., language barriers) that might affect HCWs performance in this type of continuous medical education.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".