Diversifying the Midwifery Workforce: Inclusivity, Culturally Sensitive Bridging, and Innovation
Bibliographic record
Abstract
Midwifery educators and regulators in Canada have begun to address diversity, equity, and inclusion in admission processes and program curricula. Populations served by midwives value internationally educated midwives from their countries of origin. The International Midwifery Pre-Registration Program at Ryerson University in Toronto, Ontario, provides assessment, midwifery workplace orientation, and accelerated education for internationally educated midwives on behalf of the regulatory College of Midwives of Ontario. Between 2003 and 2015, midwives from 41 countries participated in the bridging program, and 214 (80%) successfully completed the program and qualified for licensure. Of these 214 graduates, 100% passed the Canadian Midwifery Registration Examination and 193 (90%) were employed full time as midwives within 4 months of graduation. The program curriculum enables the integration of these midwives into health care workplaces utilizing innovative approaches to assessment and competency enhancement. Critical to the bridging process are simulation-based practices to develop effective psychomotor learning, virtual and real primary care community placements, and coaching in empathetic, client-centered communication. Cultural sensitivity is embedded into the multiple assessment and learning modalities, and addresses relevant barriers faced by immigrant midwives in the workplace. Findings from the 13 years of the program may be applicable to increase diversity in other North American midwifery settings. This article describes the process, content, outcomes, and findings of the program. Midwifery educators and regulators may consider the utility of these approaches for their settings.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.001 | 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".