An interprofessional education pilot program in maternity care: Findings from an exploratory case study of undergraduate students
Bibliographic record
Abstract
An interprofessional team of maternity care providers and academics developed a pilot interprofessional education (IPE) program in maternity care for undergraduate students in nursing, midwifery and medicine. There are few published studies examining IPE programs in maternity care, particularly at the undergraduate level, that examine long-term outcomes. This paper outlines findings from a case study that explored how participation in an IPE program in maternity care may enhance student knowledge, skills/attitudes, and may promote their collaborative behavior in the practice setting. The program was launched at a Canadian urban teaching hospital and consisted of six workshops and two clinical shadowing experiences. Twenty-five semi-structured, in-depth interviews were completed with nine participants at various time points up to 20 months post-program. Qualitative analysis of transcripts revealed the emergence of four themes: relationship-building, confident communication, willingness to collaborate and woman/family-centered care. Participant statements about their intentions to continue practicing interprofessional collaboration more than a year post-program lend support to its sustained effectiveness. The provision of a safe learning environment, the use of small group learning techniques with mixed teaching strategies, augmented by exposure to an interprofessional faculty, contributed to the program's perceived success.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".