A novel online training program for sexual and gender minority health increases allyship in cisgender, heterosexual paramedics
Bibliographic record
Abstract
Introduction: Sexual and gender minorities (SGM) make up 4% of the Canadian population. Due to existing barriers to care in the community, SGM patients may seek more help and be sicker at presentation to hospital. Paramedics occupy a unique role and can remove or decrease these barriers. There are no existing evaluations of training programs in SGM health for prehospital providers. A training program to develop better allyship in paramedics toward SGM populations was developed and assessed. Methods: A 70- to 90-min mandatory, asynchronous, online training module in SGM health in the prehospital environment was developed and delivered via the emergency medical service (EMS) system's learning management system. A before-and-after study of cisgender, heterosexual, frontline paramedics was performed to measure the impact of the training module on the care of SGM patients. The validated Ally Identity Measure (AIM) tool was used to identify success of training and includes subscales of knowledge and skills, openness and support, and oppression awareness. Demographics and satisfaction scores were collected in the posttraining survey. Matched and unmatched pairs of surveys and demographic associations were analyzed using nonparametric statistics. Results: = 344) were similar in demographics and scores. Rural paramedics also had significantly lower pretraining oppression awareness scores and had lower posttraining AIM scores compared to suburban paramedics (6% difference). Satisfaction scores rated the training as relevant and applicable (87% and 82%, respectively). Conclusions: A novel prehospital training program in the care of SGM patients resulted in a statistically significant increase in allyship in cisgender, heterosexual-identified frontline paramedics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".