Midwives’ integration of post abortion manual vacuum aspiration in the Democratic Republic of Congo: a mixed methods case study & positive deviance assessment
Bibliographic record
Abstract
BACKGROUND: Despite a recognized need for midwives to provide post abortion care, there exist barriers preventing them from integrating lifesaving skills such as manual vacuum aspiration (MVA) into practice. This collaborative research with the Professional Association of Congolese Midwives (SCOSAF), sought to understand how certain midwives in the Democratic Republic of Congo (DRC) have overcome barriers to successfully integrate MVA for post abortion care. Specifically, in order to provide locally-driven solutions to the problem of inadequate post abortion care in the DRC, this study aimed to identify examples of positive deviance, or midwives who had successfully integrated MVA in complex working environments following an in-service training facilitated by their midwifery association, SCOSAF. METHODS: Creswell's mixed method comparative case study design was used to identify positive deviant midwives who had practiced MVA one or more times post training and to explore their strategies and enabling factors. Other midwives who had not practiced MVA post training permitted for a comparison gro cup and further interpretations. Sources of data included a sequential survey and semi-structured interviews. RESULTS: All 102 midwives invited to be surveyed were recruited and 34% reported practicing MVA post training (positive deviant midwives). No statistical significance was found between the two groups' demographics and practice facility type. Overall, both groups had positive attitudes regarding midwifery-led MVA and legalization of abortion. Positive deviant midwives demonstrated and described more confidence and competence to practice and teach MVA. They were more likely to identify as teachers and overcome interprofessional barriers by teaching MVA to physicians, medical students and other midwives and position themselves as experts during post abortion emergencies. CONCLUSION: Results provided important insight to midwives' integration of post abortion care in Kinshasa. Strategies used by positive deviant midwives in emergencies allowed them to navigate challenging contexts in order to practice MVA, while simultaneously increasing the credibility of their profession and the dissemination of evidenced-based MVA practice. Programs designed to work with and promote positive deviant midwives as knowledge brokers could be tested for their overall impact on the diffusion of midwifery-led MVA to improve access to safe, respectful reproductive care.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".