Understanding the roles of midwives in political and health systems
Bibliographic record
Abstract
There is a lack of conceptual clarity regarding the drivers of midwives’ roles within health systems, which has contributed to the significant variability both within and across countries in whether, to what extent and how midwives are integrated in these systems. This dissertation incorporated a mix of methodological approaches to address this gap. First, a critical interpretive synthesis was used to develop a theoretical framework that identifies the different types of policy levers that would be required to enhance to roles of midwives within any given health system, and an exploratory network analysis was used to analyze relationships among the ‘health system arrangements’ part of the framework and to identify gaps in the literature. Second, a logistic regression was used to examine the correlates of birth-experience satisfaction – as a patient experience component of the health system ‘triple aim’ – among women receiving care from midwives, family physicians and/or obstetricians in Ontario’s health system. Third, an embedded single-case study design and Kingdon’s agenda setting and the 3i+E theoretical frameworks were used to qualitatively assess how and under what conditions the Ontario health system has assigned roles to midwives. The research chapters build on each other and make substantive, methodological and theoretical contributions. Specifically, insights gained from the theoretical framework informed variable selection and definition for the quantitative analysis and were tested in the embedded single-case study. Substantively, the dissertation provides a rich understanding of the roles of midwives in health systems through a mix of qualitative and quantitative research evidence, adding to the evidence base that policymakers can draw from when making decisions regarding midwifery care. v Methodologically, the dissertation introduces a novel combination of a critical interpretive synthesis and exploratory network analysis. Lastly, the dissertation advances the theoretical understanding of the roles of midwives within health systems through a new theoretical framework.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".