What are ambulance personnel experiences of sexual harassment and sexual assault in the workplace? A rapid evidence review
Bibliographic record
Abstract
INTRODUCTION: Sexual assault and harassment of ambulance personnel in the workplace is widespread. Prevention via body worn cameras and legal efforts have been positive, however improvement is still needed to ensure the protection of staff from the negative impact of sexual violence at work. METHODS: A rapid evidence review was conducted following the Cochrane Rapid Review guidance. MEDLINE and CINAHL Complete were searched from inception to February 2023. Screening and data extraction was conducted by one author and verified by the other. Included studies were appraised using a variety of critical appraisal checklists and a narrative synthesis was conducted. RESULTS: From 46 articles screened, 7 were included in the review representing 3994 ambulance personnel from Australia, Canada, the United States, the United Kingdom and South Korea. Seven themes were identified, including a need for more training, education and resources regarding sexual assault and harassment, differences in perpetrators, poor experiences with organisations, effects on victims outside the workplace, effects on victims within the workplace, barriers to reporting, and increased prevalence of sexual assault and harassment against women. CONCLUSION: The effect of sexual assault and harassment has far-reaching negative impact on victims' lives. More training and resources are recommended.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".