Knowledge acquisition after Helping Babies Survive training in rural Tanzania
Bibliographic record
Abstract
BACKGROUND: While the effectiveness of Helping Babies Breathe (HBB) training in Tanzania has been reported, no published studies of Essential Care for Every Baby (ECEB) and Essential Care for Small Babies (ECSB) in this setting have been found. This study compared knowledge before and after HBB, ECEB and ECSB training in Tanzania. METHODS: Training was provided to future facilitators (n=16) and learners (n=24) in Tanzania. Using standardized multiple-choice questions, knowledge was assessed pre- and post-HBB and ECEB courses for both learners and facilitators, while ECSB assessment was conducted with facilitators only. A >80% score was considered to be a pass. Paired t-tests were used for hypothesis testing. RESULTS: Knowledge significantly improved for both facilitators and learners on HBB and ECEB (p<0.001) and for facilitators on ECSB (p<0.001). After training, learners had difficulty identifying correct responses on one HBB item (21% incorrect) and three ECEB items (25-29% incorrect). After training, facilitators had difficulty identifying correct responses on five ECSB items (22-44% incorrect). CONCLUSIONS: Training improved knowledge in Tanzania, but not sufficiently for feeding, especially for low birthweight babies. Targeted training on feeding is warranted both within the Helping Babies Survive program and in preclinical training to improve knowledge and skill to enhance essential newborn care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".