A global scoping review to inform the recruitment and retention of Aboriginal and Torres Strait Islander nursing and midwifery academics
Bibliographic record
Abstract
To synthesise global evidence on strategies that support the recruitment and retention of First Nations nursing and midwifery academics. The representation of First Nations people in nursing and midwifery academia is essential for culturally safe healthcare education and addressing systemic inequities. Despite various initiatives, challenges persist in attracting and retaining First Nations academics in these fields. A scoping review was conducted using the Arksey and O’Malley framework and reported in line with PRISMA-ScR guidelines. A comprehensive search was conducted across six databases (Australian Indigenous Health InfoNet, Scopus, Web of Science, CINAHL, Emcare and PubMed) using terms related to Indigenous identity, academic workforce and nursing/midwifery. Studies were included if they addressed recruitment or retention of First Nations nursing and midwifery academics. The research team collaboratively extracted data to identify patterns and map key themes across the studies. Four studies met the inclusion criteria, representing Australia, Canada, New Zealand and the U.S.A. Seven key themes emerged: (1) publicly stated commitments to change, (2) curriculum-community-university partnerships, (3) defined academic pathways, (4) inclusive and culturally safe work environments, (5) mentorship and allyship, (6) supportive leadership and (7) institutional policy reform. Australian-specific research was limited despite the global relevance of the issue. Creating culturally safe, inclusive academic environments is critical to strengthening the First Nations academic workforce. Key strategies include leadership development, mentorship, institutional accountability and systemic reform. Addressing these areas is vital for improving recruitment and retention and advancing health equity outcomes. • Representation of First Nations peoples in the academic workforce, is crucial for fostering diversity, equity, and culturally safe practices in nursing and midwifery education. • The recruitment and retention of First Nations peoples into the nursing and midwifery academic workforce requires processes that are culturally safe and respectful. • A critical gap exists in the knowledge required to support the recruitment and retention of First Nations peoples in nursing and midwifery academia. • Seven key strategies are proposed to create culturally safe, inclusive academic environments to strengthen the First Nations academic workforce.
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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 | 0.055 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.047 | 0.035 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".