Can peers improve student retention? Exploring the roles peers play in midwifery education programmes in Canada
Bibliographic record
Abstract
PROBLEM: Peer support is understudied as a factor that can impact midwifery student retention. BACKGROUND: Retention of midwifery students is essential for creating a sustainable maternity care workforce. Research shows that peer support positively influences students' experiences, but it needs more focus on the role peers play in student retention. AIM: We aimed to examine how peer support can facilitate midwifery students' retention by exploring the role peers play in students' experiences and identifying the types of support students offer each other. METHODS: We conducted 31 semi-structured interviews with students attending Midwifery Education Programmes across Canada. Data were analysed inductively, following the constructivist grounded theory method. FINDINGS: While motivated and engaged peers improved students' learning experiences and desire to remain in their program, peers who created an overly competitive academic environment hindered learning. Students also noted that a lack of diversity, particularly of Black and Indigenous peers, limited their ability to learn about culturally safe care. Most students felt a sense of community and relied on one another for emotional, academic, and instrumental support. DISCUSSION: Peer support has mostly positive effects on student learning and should be formalized by midwifery educators to improve retention. Reducing pressure to succeed, targeting recruitment of students who are Black, Indigenous, and People of Colour, and establishing formal mentorship programmes could enhance the role peers play in student retention. CONCLUSION: While retention of students is a complex issue, positive interactions with peers can create a stimulating learning environment and increase students' desire to stay in their programme.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".