Health systems factors impacting the integration of midwifery: an evidence-informed framework on strengthening midwifery associations
Bibliographic record
Abstract
INTRODUCTION: Midwifery associations are organisations that represent midwives and the profession of midwifery. They support midwives to reduce maternal and newborn mortality and morbidity by promoting the overall integration of midwifery in health systems. Our objective was to generate a framework for evidence-informed midwifery association strengthening. METHODS: A critical interpretive synthesis complemented by key informant interviews, focus groups, observations, and document review was used to inform the development of concepts and theory. Three electronic bibliographical databases (CINAHL, EMBASE and MEDLINE) were searched through to 2 September 2020. A coding structure was created to guide the synthesis across the five sources of evidence. RESULTS: A total of 1634 records were retrieved through electronic searches and 57 documents were included in the critical interpretive synthesis. Thirty-one (31) key informant interviews and five focus groups were completed including observations (255 pages) and audio recordings. Twenty-four (24) programme documents were reviewed. The resulting theoretical framework outlines the key factors by context, describes the system drivers that impact the sustainability of midwifery associations and identifies the key-enabling elements involved in designing programmes that strengthen midwifery associations. CONCLUSION: Midwifery associations act as the web that holds the profession together and are key to the integration of the profession in health systems, supporting enabling environments and improving gender inequities. Our findings highlight that in order to strengthen midwifery (education, regulation and services), we have to lead with association strengthening. Building strong associations is the foundation necessary to create formal quality midwifery education systems and to support midwifery regulation and accreditation mechanisms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".