The gender wage gap in the Canadian pharmacy workforce in the wake of COVID-19
Bibliographic record
Abstract
Background: Understanding gender disparities in the way women pharmacists experience their careers is essential for robust workforce planning, as the profession has undergone substantial feminization in recent decades. This study aimed to quantify gender-related wage gaps in the Canadian pharmacy workforce as a tracer for progress toward gender equity in the wake of COVID-19. Methods: A national observational study was conducted using gender-disaggregated data among pharmacists from the 2021 population census and integrated income tax records capturing annual professional earnings in 2019 and (pandemic-affected) 2020. Descriptive and multivariate decomposition analyses were used to characterize earnings differentials, with adjustment for several professional, personal, and geographic factors. Results: Nearly two-thirds (63%) of pharmacists aged 25 to 54 were women. Despite similar levels of education, women's earnings averaged 88 cents for every dollar earned by men. A significant gender wage gap was found, with women earning 9.2% (95% confidence interval [CI]: 4.8%-13.8%) less than men on average in 2020 after adjustment for other confounders. Much of the gap was explained by the measured predictors, including gendered earnings differentials observed the previous year, but a significant residual (34% of the gap) remained unexplained in the decomposition analysis. Discussion: This first nationally representative investigation of wage differentials among Canadian pharmacists found evidence of a persistent gender-related wage gap, one that was only marginally affected by labour disruptions brought on by the COVID-19 pandemic and which was largely unexplained-an outcome commonly attributed, at least in part, to gender bias and discrimination. Conclusion: Concerted efforts are needed among multiple stakeholders for achieving women's full economic inclusion in the pharmacy profession.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".