Characteristics of strong midwifery leaders and enablers of strong midwifery leadership: An international appreciative inquiry
Bibliographic record
Abstract
OBJECTIVES: This research aimed to identify the characteristics of strong midwifery leaders and explore how strong midwifery leadership may be enabled from the perspective of midwives and nurse-midwives globally. DESIGN: In this appreciative inquiry, we collected qualitative and demographic data using a cross-sectional online survey between February and July 2022. SETTING: Responses were received from many countries (n = 76), predominantly the United Kingdom (UK), Australia, the United States of America (USA), Canada, Uganda, Saudi Arabia, Tanzania, Rwanda, India, and Kenya. PARTICIPANTS: An international population (n = 429) of English-speaking, and ethnically diverse midwives (n = 211) and nurse-midwives (n = 218). MEASUREMENTS: Reflexive thematic analysis was used to make sense of the qualitative data collected. Identified characteristics of strong midwifery leadership were subsequently deductively mapped to established leadership styles and leadership theories. Demographic data were analysed using descriptive statistics. FINDINGS: Participants identified strong midwifery leaders as being mediators, dedicated to the profession, evidence-based practitioners, effective decision makers, role models, advocates, visionaries, resilient, empathetic, and compassionate. These characteristics mapped to compassionate, transformational, servant, authentic, and situational leadership styles. To enable strong midwifery leadership, participants identified a need for investment in midwives' clear professional identity, increased societal value placed upon the midwifery profession, ongoing research, professional development in leadership, interprofessional collaborations, succession planning and increased self-efficacy. KEY CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: This study contributes to understandings of trait, behavioural, situational, transformational and servant leadership theory in the context of midwifery. Investing in the development of strong midwifery leadership is essential as it has the potential to elevate the profession and improve perinatal outcomes worldwide. Findings may inform the development of both existing and new leadership models, frameworks, and validated measurement tools.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".