RCPNEOPERU trial: a cluster randomized pilot trial to assess traditional neonatal resuscitation compared to partially virtual training in remote areas
Bibliographic record
Abstract
OBJECTIVE: To assess the effects of a neonatal resuscitation training program using traditional training and partial distance learning. METHOD: Through an open cluster-randomized trial, the authors compared a traditional approach involving face-to-face theory and practice sessions using information and communication technology to offer theory and distance examination, followed by face-to-face practice. Twelve health facilities were allocated by blocked randomization. Comparisons were made adjusting for clustering in qualitative and quantitative data. The primary outcome was the percentage of infants with heart rates ≥100 per minute at the second minute after birth. The authors performed a cluster-level analysis for cluster randomized trials, simplifying the adjustment for individual- and cluster-level covariates. RESULTS: The authors trained 403 health professionals in two arms in twelve facilities. After six months, the authors assessed 2180 birth deliveries, 966 newborns in the traditional training group (TT), and 1214 in the partial distance learning training group (pDL). The authors found no statistical evidence favoring any of the two trial arms (RR = 0.9859, CI 95 % = 0.9446; 1.0292, p = 0.4819). CONCLUSIONS: The authors found no statistical evidence favoring traditional or distance learning methods for neonatal resuscitation training. Further research could assess improved online platforms to enable sustainable virtual reality instructor/provider interaction for theory, practice and testing, addressed to health cadres of rural and remote areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".