Intersectionality of the Gender Wage Gap Among Healthcare Professionals: A Scoping Review
Bibliographic record
Abstract
Background: A growing body of research has documented persistent wage gaps between women and men in the healthcare workforce, a pattern widely observed across cadres and countries. Less well known is whether various intersecting characteristics often associated with social discrimination may exacerbate or attenuate gendered disparities. This review scopes contemporary research from diverse settings focusing on how race, ethnicity, and sexual and gender minority status may intersect in shaping earnings differentials among healthcare practitioners to help inform policy and management decisions. Methods: Studies quantifying the intersecting axes of gender and other postulated social drivers of differed practitioner earnings were identified by systematically searching five bibliographic databases (Embase, CINAHL, EconLit, SocIndex, and PsychInfo) and scanning the reference lists of review articles and other forms of the global health literature. A total of 2123 reports were retrieved; after screening, 21 articles were retained for narrative synthesis. Results: The studies covered data from four countries (Brazil, Norway, the United Kingdom, and the United States). Physicians were researched most often (43% of the synthesized articles) followed by nurses (38%). No uniform patterns were found in gendered earnings variations stratified by race, ethnicity, and/or ancestry; however, wide variations were seen in the way the relationships were operationalized across studies and contexts. One investigation included sexual orientation as a factor in earnings gaps, but presented results combined with other personal characteristics. None of the studies examined wage data by gender minority status. Conclusions: This review highlighted notable limitations in the available research in relation to disaggregated measures of ethnocultural heterogeneity, robust methodologies and transparent reporting, and the underlying health workforce information systems for incorporating more diversity elements and enhancing cross-national comparability in assessments of structural wage gaps among healthcare practitioners.
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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 | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".