Peer Learning and Mentorship for Neonatal Management Skills: A Cluster-Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Clinical knowledge and skills acquired during training programs like Helping Babies Breathe (HBB) and Essential Care for Every Baby (ECEB) decay within weeks or months. We assessed the effect of a peer learning intervention paired with mentorship on retention of HBB and ECEB skills, knowledge, and teamwork in 5 districts of Uganda. METHODS: We randomized participants from 36 Ugandan health centers to control and intervention arms. Intervention participants received HBB and ECEB training, a 1 day peer learning course, peer practice scenarios for facility-based practice, and mentorship visits at 2 to 3 and 6 to 7 months. Control arm participants received HBB and ECEB training alone. We assessed clinical skills, knowledge, and teamwork immediately before and after HBB/ECEB training and at 12 months. RESULTS: Peer learning (intervention) participants demonstrated higher HBB and ECEB skills scores at 12 months compared with control (HBB: intervention, 57.9%, control, 48.5%, P = .007; ECEB: intervention, 61.7%, control, 49.9%, P = .004). Knowledge scores decayed in both arms (intervention after course 91.1%, at 12 months 84%, P = .0001; control after course 90.9%, at 12 months 82.9%, P = .0001). This decay at 12 months was not significantly different (intervention 84%, control 82.9%, P = .24). Teamwork skills were similar in both arms immediately after training and at 12 months (intervention after course 72.9%, control after course 67.2%, P = .02; intervention at 12 months 70.7%, control at 12 months 67.9%, P = .19). CONCLUSIONS: A peer learning intervention resulted in improved HBB and ECEB skills retention after 12 months compared with HBB and ECEB training alone.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".