‘Mob aren’t staying when there’s no support’: Enablers and barriers of recruitment and retention of First Nations midwifery students – A qualitative study
Bibliographic record
Abstract
BACKGROUND: Aboriginal and Torres Strait Islander (hereafter referred to as First Nations) childbearing women report negative experiences from a lack of culturally safe maternity care. Evidence supports improved health outcomes for First Nations women and infants when cared for by First Nations midwives. There are barriers to First Nations students accessing university, particularly nursing and midwifery students, with a lack of evidence exploring the experiences of First Nations midwifery students. AIM: This study aims to understand the impact of the current strategies to improve recruitment and retention of First Nations midwifery students and identify further innovations. METHODS: A semi-structured yarning circle was held with six Bachelor of Midwifery students at a university in Queensland, Australia. FINDINGS: Three key categories emerged: student recruitment, student retention and student success. Enablers included culturally appropriate recruitment, partnerships with other First Nations peoples, incorporating First Nations ways of Knowing, Being, and Doing, culturally safe support, placements and mentorship, and identification and representation. Barriers included financial impacts, experiences of racism and lack of Cultural Safety and humility. DISCUSSION: Overall, students felt the university provided a culturally safe environment and implemented strategies that supported students' recruitment, retention and success in the degree. They suggested improvements to current strategies and new ideas for implementation. CONCLUSION: Strategies to improve recruitment and retention of First Nations midwifery students are imperative to close the gap in educational attainment and improve health outcomes for First Nations peoples. These strategies need to be multi-layered, culturally appropriate and implement a whole of university approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".