Women in Healthcare: Barriers and Enablers from a Developing Country Perspective
Bibliographic record
Abstract
BACKGROUND: As the under-representation of women in management positions continues to persist globally, little is known about the experiences of women in the healthcare sector in the context of the developing Middle Eastern nations. In an attempt to address this knowledge gap, the current study explores some of the barriers that hinder and the enablers that foster women's career advancement in the healthcare sector. To meet its objectives, the current study uses a relational approach that integrates the macro socio-cultural, meso-organisational, and micro-individual levels of analysis. METHODS: Guided by institutional theory as a theoretical framework and social constructionism as a philosophical stance, the current study adopts a qualitative research methodology. It capitalizes on in-depth, semi-structured, face-to-face interviews with women managers in different occupational fields, across the managerial hierarchy in the healthcare sector in Lebanon. Snowballing and purposeful sampling procedures were used, and the interviews were analysed using thematic analysis, focusing on identifying new, emerging themes. RESULTS: The results of the study confirm the salience of discriminatory cultural values, gendered social roles and expectations in Middle Eastern societies, and illustrate their role as barriers hindering women's career advancement. The results also portray the spillover effect of societal expectations and cultural gender stereotypes into the organisational realm, resulting in widely experienced attitudinal and structural organisational barriers. This study also illustrates how the enablers that facilitate and promote women's career progression unfold amidst the interplay between the macro and meso factors, lending credence to the role of women's agency at the individual micro level. Amongst the toll of barriers, Middle Eastern women navigate the patriarchy of their cultures and the discrimination inherent in their organisations by using their agency and persistence as they construct and negotiate their careers in management. CONCLUSION: This study provides new knowledge on the status of Middle Eastern women in the healthcare sector, a sub-category of female employees that to date, is under-researched. It primarily highlights the role of agency in building women's careers. It also stresses the notion that the complexity of women's careers in the healthcare sector can be best understood using a relational approach that highlights the intersectionality between gender, agency, socio-cultural realities and organisational boundaries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".