Women’s leadership in the healthcare landscape. Original evidence from an innovative narrative review of the literature: the female-led study
Bibliographic record
Abstract
The “great man” theory inherently excludes women, as it traditionally focuses on leadership features associated with men. In recent years, the healthcare sector has experienced a growing presence of women in leadership roles; however, although female health workers significantly outnumber men, the number of women leaders remains lower than that of men. This article seeks to investigate potential differences between male and female leadership, identify the winning characteristics of female leadership, and examine the barriers and obstacles that may preclude women’s access to leadership positions. A review of existing reviews available on PubMed was conducted using specific search queries. The authors analyzed the selected articles according to specific inclusion and exclusion criteria, using the PICO methodology. Out of 967 articles, 18 met the inclusion criteria. The most frequently identified characteristics of female leadership included a democratic and non-individualistic style, strong communication skills, and empathy. The most common obstacles to the advancement of female leadership included lower compensation, persistent stereotypes and prejudices, and insufficient support from institutions in addressing the gender gap. Academic studies confirm that women tend to adopt a transformational leadership style, in contrast to the more autocratic and assertive male leadership. Further research on female leadership is essential for monitoring progress and fostering actions that allow women to thrive in top leadership positions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".