Continuous training and certification in neonatal resuscitation in remote areas using a multi-platform information and communication technology intervention, compared to standard training: A randomized cluster trial study protocol
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background</ns4:bold> : About 10% of all newborns may have difficulty breathing and require support by trained personnel. In Peru, 90% of deliveries occur in health facilities. However, there is not a national neonatal resuscitation and certification program for the public health sector. In addition, the Andes and the Amazon regions concentrate large rural remote areas, which further limit the implementation of training programs and the accomplishment of continuous certification. Neonatal resuscitation training through the use of information, communication and technology (ICT) tools running on computers, tablets or mobile phones, may overcome such limitations. This strategy allows online and offline access to educational resources, paving the way to more frequent and efficient training and certification processes. </ns4:p> <ns4:p> <ns4:bold>Objective</ns4:bold> : To evaluate the effects of a neonatal resuscitation training and certification program that uses a multi-platform ICT (MP-ICT) strategy on neonatal health care in remote areas. </ns4:p> <ns4:p> <ns4:bold>Methods</ns4:bold> : We propose to conduct the study through a cluster-randomized trial, where the study and analysis unit is the health care facility. Eligible facilities will include primary and secondary health care level facilities that are located in provinces with neonatal mortality rates higher than 15 per 1,000 live births. We will compare the proportion of newborns with a heart rate ≥100 beats per minute at two minutes after birth in health care facilities that receive MP-ICT training and certification implementation, with those that receive standard training and certification. </ns4:p> <ns4:p> <ns4:bold>Discussion</ns4:bold> : We expect that the intervention will be shown as more effective than the current standard of care. We are prepared to include it within a national neonatal resuscitation training and certification program to be implemented at national scale together with policymakers and other key stakeholders. </ns4:p> <ns4:p> <ns4:bold>Trial registration</ns4:bold> : ClinicalTrials.gov Nº NCT03210194 </ns4:p> <ns4:p> <ns4:bold>Status of the study</ns4:bold> : This study is enrolling participants by invitation only. </ns4:p> <ns4:p>Study protocol version 1.1 – March 31st, 2017</ns4:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".