A Pilot Project of Collaborative Maternity Education: Understanding Perspectives from Family Medicine and Midwifery
Bibliographic record
Abstract
Background: In Canada, the decreasing numbers of family physicians and the small number of midwives providing obstetric care have been associated with a decline in access to maternity services. Several studies and policy documents support the development of models to enhance collaboration between midwives and physicians and to expose trainees to these models. A pilot project was undertaken to implement and evaluate an interprofessional learning opportunity involving midwifery students (MWSs) and family medicine residents (FMRs). Methods: The aim was to describe how FMRs and MWSs develop skills to collaborate, and to identify the feasibility of this type of education. A convenience sample of 12 FMRs and 6 MWSs in a southern Ontario suburban community and their preceptors participated in a series of educational seminars and a clinical placement within the midwifery practice. Qualitative focus groups and interviews were conducted, and data were analyzed using thematic analysis. Results: Qualitative analysis highlighted themes relating to the engaging of learners, logistical challenges, and the perceived value of interprofessional education (IPE).Conclusions: This pilot project highlights barriers to and enablers of IPE. The findings will inform the modification of the project for future use and suggest that this project could be a useful model of IPE for primary maternity care.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".