Exploring first nations nursing and midwifery leadership development: an international scoping review
Bibliographic record
Abstract
BACKGROUND: The development of First Nations nurses and midwives is crucial to addressing health inequities stemming from systemic injustices. However, this workforce is significantly underrepresented globally. Understanding the reasons for this underrepresentation and identifying key challenges and opportunities for leadership is necessary. AIM: This scoping review aimed to explore the challenges and opportunities in leadership development of First Nations nursing and midwifery professionals internationally. DESIGN: A scoping review was conducted following the framework developed by Arksey and O'Malley (2005). DATA SOURCES: were searched. METHODS: The search was performed on 30 January 2024. Items were included if the research focus was on First Nations nursing and midwifery leadership. Full texts were then thematically analysed for overarching themes, and extracted data was charted. After charting, key findings were reviewed, and emerging themes were grouped into common categories. RESULTS: The scoping review identified a paucity in the contemporary literature, with only ten articles retrieved. Analysis revealed five main theses: (1) systemic injustices impacting leadership opportunities, (2) complex responsibilities beyond typical roles, (3) underrepresentation in leadership positions, (4) shifting from colonial leadership models and (5) effective methods for leadership development. Opportunities identified included promoting equitable leadership, fostering integrated relationships, building cultural resilience and emphasising community-orientated leadership approaches. CONCLUSION: Promoting adequate representation and developing culturally safe leadership models are essential steps towards empowering First Nations nurses and midwives in their leadership development. The study highlights the need for targeted leadership development strategies for First Nations nurses and midwives to enhance representation and impact within healthcare systems globally.
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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 | 0.019 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.025 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".