Moving beyond awareness: The evidence on sexism within paramedicine is in – it's time to transform the system
Bibliographic record
Abstract
The current landscape of gender-based inequity and everyday sexism experienced by women-identifying paramedics has been under intensified scrutiny over the past decade. Empirical research, organisational culture scans and advocacy from special interest groups have collectively pushed the profession into a stage of confrontation with its cultural shortcomings. Yet, despite the depth and clarity of evidence, the pace of structural change continues to lag behind compared with implementing other evidence-informed priorities within the profession. Priorities reflect values. Recognition is not enough; now is the time to move from conceptual commitments to practical action, and from awareness to accountability in order to eradicate everyday sexism and gender-based violence in paramedicine. In this commentary, we set out clear, actionable proposals to help transform the profession from within. These include gender equity auditing and transparent reporting, stronger leadership accountability, enforced anti-harassment standards and the meaningful inclusion of structurally marginalised voices. These aren’t peripheral suggestions; they are central to shifting paramedicine from performative inclusion towards genuine, systemic reform. By bringing these actions to the forefront, we aim to focus attention on what must change and who that change is for.
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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 | 0.025 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".