Bibliographic record
Abstract
Women constitute most health workers, yet they hold proportionately fewer leadership positions. The literature is replete with normative advice to address gender specific barriers to women’s leadership; less attention is paid to the processes women undertake on their paths to leadership. We describe the leadership journeys of 23 women leaders in the health sector in Canada, guided by a multi-layered framework of barriers and enablers. A thematic analysis of 11 semi-structured interviews and 13 public presentations on leadership journeys was conducted, which applied a priori and emerging themes to segments of the transcripts using NVivo 12. Three key themes emerged: impetus for leadership journey, enablers to leadership development, and barriers to advancement. Women leaders reported a variety of reasons to embark on their leadership journey from their own desire to make a difference to being tapped on the shoulder by mentors and sponsors. Many of the barriers faced were specific to their intersectional identities where they often juggled the complex demands of gender role expectations, while maintaining personal and familial mental health and well-being. The multi-layered framework of important factors was validated and improved. Better understanding women’s leadership journeys needs to capture processual and structural dimensions. Keywords: Women Leaders, Leadership Journeys, Health Care, Health Sciences
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".