Gender and Pay Gaps in Paramedic Services Leadership in Ontario, Canada
Bibliographic record
Abstract
Objectives Research suggests that women are underrepresented in healthcare leadership and often earn less than men. This may be true in the emergency medical services (EMS) as well, but research on the subject is limited and specific data in Canada is scarce. This study aimed to estimate the gender and income distributions among leadership within Ontario’s paramedic services.Methods We abstracted records for leadership positions (e.g., superintendent, commander, deputy chief, chief) from the Ontario Public Sector Salary Disclosure List. Two raters independently assessed the presumed binary gender of each individual, resolving discrepancies through consensus. Interrater agreement was measured using a kappa statistic. Chi-square tests compared the proportions of men and women at different leadership levels (entry, middle, executive). Income distributions were compared using parametric and non-parametric tests, stratified by leadership level.Results Our search yielded 863 individuals from 49 (out of 54) paramedic services. Interrater agreement on presumed gender was 95% (κ = 0.87, p < 0.001). After resolving discrepancies (n = 43), we achieved complete agreement for 855 individuals (98%). Among the sample, 655 (76%) were presumed to be men. Women held 23% of entry, 35% of middle, and 15% of executive leadership roles. Within the leadership pool and compared to men, women were twice as likely to hold a middle leadership role (Odds Ratio [OR] 2.00, 95% Confidence Interval [CI] 1.35–2.98, p < 0.001) but less likely to hold an executive leadership position (OR 0.54, 95% CI 0.33–0.87, p = 0.012). Median income distributions were comparable at the executive level (p = 0.327), but lower for women at the middle and entry leadership levels, earning $0.90 (p < 0.001) and $0.95 (p < 0.001) for every dollar earned by men, respectively. Gender accounted for 1.7% of the variance in total earnings.Conclusions Our findings suggest the existence of both gender and pay gaps in leadership, the reasons for which are not immediately apparent and warrant further study.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".