“Patriarchy permeating health policymaking”: Influence of gender on involvement in health policymaking from nurse leaders' perspective
Bibliographic record
Abstract
Abstract Despite a greater percentage of women in the healthcare workforce, women are underrepresented in leadership positions. Researchers have examined the influence of gender on women involvement in policy‐making and leadership in male‐dominated professions. However, no research has explored nurses' perspectives about the role of gender in impacting their involvement in health policymaking in female‐dominant profession. This study explores nurse leaders' perspectives on how gender can influence their involvement in health policymaking in Pakistan. Eleven nurse leaders with at least 1 year of experience in policymaking participated in semi‐structured interviews. The data were analyzed using reflexive thematic analysis. Four themes emerged: Patriarchy Permeates Health Policymaking; Women's Social Status and Nurses' Involvement in Policymaking; Intentionally Disregarding Nurses' Insights on Policy Forums; Condescending Attitudes Towards Women Nurses on Policy Forums. The underrepresentation of nurses in health policymaking is influenced by gender and social biases and stereotypes against women and the negative social image of the nursing profession. Health‐care organizations must play an active role and develop policies to combat gender‐based discrimination and curb the underrepresentation of nurses in healthcare policymaking.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".