Improving spaces for women first responders: A grounded theory on gender equity
Bibliographic record
Abstract
Emergency response work has historically been performed by men and thus designed with them in mind; however, during the past few decades, increasing numbers of women are conducting this work. Despite growing participation, research suggests women first responders continue to face unsupportive workplace structures and cultures. This study explored the occupational experiences of women who work as firefighters, police officers, and paramedics from Southern Ontario, Canada. Semi-structured interviews conducted with this population (n = 20) focused on resiliency and stress, diversity and inclusion, and gender and the role of professional identity. Constructivist grounded theory guided analysis and cross-profession comparisons. Participants described significant improvements to women's inclusion in first response work, however, they also identified continuing challenges. While some environments were described as highly supportive, many women still faced sexism and glass ceilings. Despite persisting obstacles, participants were deeply passionate about their work, and actively encouraged other women to join the field. Study results suggest that future advances can be encouraged by addressing the need for improved access to uniforms and equipment, on-the-job training to address barriers to promotions, flexible scheduling and childcare supports, and legislating equity, diversity, and inclusion training for all leaders and workers in the first responder community.
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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 | 0.024 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".