Gender composition and wage gaps in the Canadian health policy research workforce in comparative perspective
Bibliographic record
Abstract
BACKGROUND: Gendered challenges have been shown to persist among health practitioners in countries at all levels of development. Less is known about non-clinical professionals, that is, those who do not deliver services directly but are essential to health systems performance, such as health policy researchers. This national observational study examined gender occupational segregation and wage gaps in the Canadian health policy research workforce using a cross-domain comparative labour market analysis approach. METHODS: Sourcing data from the 2016 population census, we applied linear regression and Oaxaca-Blinder decomposition techniques to assess wage differentials by sex, traditional human capital measures (e.g., age, education, place of work), and social identity variables intersecting with gender (household head, childcare, migrant status) among health policy researchers aged 25-54. We compared the gender composition and wage gap with seven non-health policy and programme domains, as mapped under the national occupational classification by similarity in the types of work performed. RESULTS: The health policy research workforce (N = 19 955) was characterized by gender segregation: 74% women, compared with 58% women among non-health policy research occupations (N = 102 555). Women health policy researchers earned on average 4.8% (95% CI 1.5‒8.0%) less than men after adjusting for other professional and personal variables. This gap was wider than among education policy researchers with similar gender composition (75% women; adjusted wage gap of 2.6%). Wages among health policy researchers were 21.1% (95% CI 19.4‒22.8%) lower than their counterparts in the male-dominated economics policy domain, all else being equal. Overall, women's earnings averaged 3.2% lower than men's due to factors that remained unexplained by policy domain or other measured predictors. CONCLUSIONS: This investigation found that the gender inequalities already widely seen among clinical practitioners are replicated among health policy researchers, potentially hindering the competitiveness of the health sector for attracting and retaining talent. Our findings suggest intersectoral actions are necessary to tackle wage gaps and devaluation of female-dominated health professions. Accountability for gender equity in health must extend to the professionals tasked with conducting equity-informative health policy research.
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 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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".