The inclusion of racialized women in the nursing workforce
Bibliographic record
Abstract
Purpose The study focuses on employment equity among Canadian women with a nursing education, examining differences across racialized groups. Design/methodology/approach The analysis used data from the 2021 Canadian Census of Population on a large sample of women aged 25–64 years with a nursing education (n = 112,000). The analysis compared women from ten racialized population groups to those from the White population group on attainment of a nursing education, employment in the health sector, and having an occupation that matched their nursing education. These comparisons were made separately for women who were Canadian-born, Canadian-educated immigrants and foreign-educated immigrants and controlled for differences in educational and demographic characteristics. Findings Most racialized women were under-represented in terms of having a nursing education, which was a barrier to their inclusion in the nursing workforce. Having a Canadian nursing education eliminated most disparities between racialized and White women in terms of employment outcomes. Foreign-educated immigrant women experienced a large penalty in levels of workforce integration, and this penalty was mostly larger for those from racialized population groups than the White population group. Large proportions of foreign-educated immigrant women with a nursing education had non-health occupations or health occupations that underutilized their skills. Originality/value This study provides a granular perspective on disparities between racialized and White women in levels of employment and utilization in the nursing workforce. The analyses illustrate the need for disaggregated data to reveal where the disparities lie and the context in which these disparities emerge.
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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 | 0.006 | 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 | 0.019 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.013 |
| 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".