Developing a Canadian midwifery research priority framework: a Delphi consensus study
Bibliographic record
Abstract
INTRODUCTION: Globally, there is a commitment to evidence informed midwifery practice. In Canada, the midwifery profession has grown significantly over the past three decades, yet Canadian midwifery research lacks coordination and infrastructure. Developing a unified research agenda to address emerging clinical, population and policy challenges is essential for advancing research capacity building. METHODS: This study employed a modified Delphi technique, a robust consensus-seeking method, to identify national research priorities for Canadian midwifery. The process included a scoping review, a cross-sectional national survey, regional focus groups and follow-up surveys. Participants included midwives, researchers, service users, students and policymakers. Data were analysed using both qualitative and quantitative methods to identify and rank priority areas. RESULTS: The study identified three primary research priority areas: organizing models of care, optimizing reproductive care and strengthening the profession. Key themes included access to care, expanding midwifery roles, informed choice and addressing systemic barriers. Facilitating factors such as effective collaboration, policy changes and building research capacity were also highlighted. The final framework emphasizes the need for coordinated efforts to enhance the quality and accessibility of midwifery services across Canada. CONCLUSIONS: The findings underscore the importance of a coordinated national research agenda to support the growth and development of midwifery in Canada. By focusing on identified priority areas and facilitating factors, the midwifery profession can continue to provide high-quality, equitable care. The study's methodology and results can inform similar efforts globally, promoting the integration of midwifery care into health systems worldwide.
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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 | 0.226 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".