Challenges encountered by midwives performing basic neonatal resuscitation in health facilities in Kinshasa, Democratic Republic of the Congo
Bibliographic record
Abstract
Worldwide, an estimated five million children under the age of five die each year; 47% of these deaths occur during the neonatal period, and the vast majority in low- and middle-income countries. Events during labor are the cause of one quarter of neonatal deaths globally. Basic resuscitation with positive pressure ventilation reduces these deaths but is challenging to execute. To characterize barriers to implementation of basic neonatal resuscitation, we conducted a qualitative study using focus group discussions with midwives at three health facilities in Kinshasa, Democratic Republic of the Congo. We analyzed qualitative data using an inductive content approach in order to identify emergent themes and trends. Twenty-four midwives participated with a median age of 49 and over 80% with more than 10 years of clinical experience. We categorized challenges to implementing basic neonatal resuscitation into three themes with subthemes: 1) limited resources (subthemes: human resource limitations, inadequate and unprepared equipment, insufficient monitoring during labor); 2) inadequate simulated and clinical experience (subthemes: poor systems to support maintenance of skills, infrequent opportunity to resuscitate); 3) emotional burden of resuscitation (subthemes: decision-making under time pressure, tendency to stick to the routine, acute stress during resuscitation, moral distress after unsuccessful outcome). Our findings suggest that while simulation training is key, learning from clinical events may be a critical companion to address these barriers. We call for a new focus on developing and evaluating strategies that support providers in learning from every newborn resuscitation.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".