Women’s Participation in Leadership Roles in a Single Canadian Paramedic Service
Bibliographic record
Abstract
Introduction Like other public safety professions, paramedicine has historically been a male-dominated occupation, both in the demography of its workforce and in its organizational culture. Although women are increasingly choosing paramedicine as a career, participation in leadership roles remains limited. Drawing on data from a recent comprehensive mental health survey, we describe the proportion of women in leadership in a single, large, urban paramedic service in Ontario, Canada. Methods We distributed an in-person, paper-based survey during the fall 2019 - winter 2020 Continuing Medical Education (CME) sessions. Participating paramedics completed a demographic questionnaire alongside a battery of mental health screening tools. We assessed the demography of the workforce and explored differences in employment classification, provider level (e.g., primary vs. advanced care), and participation in formal leadership roles along self-reported gender lines. Results Out of 607 paramedics attending CME, we received 600 completed surveys, with 11 excluded for missing data, leaving 589 for analysis and a 97% response rate. Women comprised 40% of the active-duty paramedic workforce, with an average of 8 years of experience. Compared to men, women were more than twice as likely to have a university degree (Odds Ratio [OR] 2.02), but almost half as likely to practice at the Advanced Care Paramedic level (OR 0.61), and somewhat less likely to be employed full-time (OR 0.77). Women were nearly 70% less likely to hold a leadership role in the service compared to men (OR 0.36), occupying just 20% of leadership positions. Conclusion Although paramedicine is witnessing an encouraging shift in the demography of its workforce with greater participation from women, there is still work to be done, particularly in leadership. Future research should focus on identifying and ameliorating barriers to career advancement among women and other historically underrepresented people.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 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) | 0.003 | 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".