Midwives’ experience of participating in the Advanced Life Support in Obstetrics® educational program in Rwanda
Bibliographic record
Abstract
High maternal and newborn mortality rates remain a global health issue. Ninety-nine percent of maternal deaths occur in low and middle income countries and many could be prevented by having more qualified health providers. In 2013, 62% of maternal deaths took place in Sub-Saharan Africa. The Advanced Life Support in Obstetrics (ALSO ® ) Educational Program is an internationally recognized continuous professional development course aimed at increasing the knowledge, skills, competence, and confidence of health professionals to manage obstetric emergencies. The purpose of this qualitative descriptive study was to explore midwives’ experiences of translating the knowledge and skills acquired from participating in the ALSO ® program into their professional practice in Rwanda. A purposive sample of nine midwives participated in one-to-one interviews directed at understanding their experience of implementing their new knowledge and skills into practice. All interviews were audio-recorded and transcribed verbatim. Content analysis was used to illuminate five themes: 1) Improved midwifery practice, 2) Availability of resources, 3) Inter-professional collaboration, 4) Job (dis)satisfaction, and 5) Autonomy for midwifery practice. The findings revealed that although midwives reported increased knowledge, skills and confidence in management of obstetric emergencies, their ability to change practice was often hampered by non-conducive work environments, a shortage of health care providers, and insufficient equipment and materials. These findings can serve to inform ALSO ® course module development, midwifery education development, and health human resources policy that can address obstetrical and newborn education needs and health service delivery in Rwanda.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".