Barriers and facilitators related to implementation of regulated midwifery in Manitoba: a case study
Bibliographic record
Abstract
BACKGROUND: In 2000, midwifery was regulated in the Canadian Province of Manitoba. Since the establishment of the midwifery program, little formal research has analyzed the utilization of regulated midwifery services. In Manitoba, the demand for midwifery services has exceeded the number of midwives in practice. The specific objective of this study was to explore factors influencing the implementation and utilization of regulated midwifery services in Manitoba. METHODS: The case study design incorporated qualitative exploratory descriptive methods, using data derived from two sources: interviews and public documents. Twenty-four key informants were purposefully selected to participate in semi-structured in-depth interviews. All documents analyzed were in the public domain. Content analysis was employed to analyze the documents and transcripts of the interviews. RESULTS: The results of the study were informed by the Behavioral Model of Health Services Use. Three main topic areas were explored: facilitators, barriers, and future strategies and recommendations. The most common themes arising under facilitators were funding of midwifery services and strategies to integrate the profession. Power and conflict, and lack of a productive education program emerged as the most prominent themes under barriers. Finally, future strategies for sustaining the midwifery profession focused on ensuring avenues for registration and education, improving management strategies and accountability frameworks within the employment model, enhancing the work environment, and evaluating both the practice and employment models. Results of the document analysis supported the themes arising from the interviews. CONCLUSION: These findings on factors that influenced the implementation and integration of midwifery in Manitoba may provide useful information to key stakeholders in Manitoba, as well as other provinces as they work toward successful implementation of regulated midwifery practice. Funding for new positions and programs was consistently noted as a successful strategy. While barriers such as structures of power within Regional Health Authorities and inter and intra-professional conflict were identified, the lack of a productive midwifery education program emerged as the most prominent barrier. This new knowledge highlights issues that impact the ongoing growth and capacity of the midwifery profession and suggests directions for ensuring its sustainability.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| 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".