“The questions made me realize how many times I could have been saved and removed from that situation”: The experiences of patients attended to by paramedics for intimate partner violence, and actionable implementations for paramedicine
Bibliographic record
Abstract
INTRODUCTION: Intimate partner violence (IPV) persists as a severe and prevalent criminal, social, and health issue, most commonly affecting women. Survivors of IPV frequently engage with the healthcare system to access treatment, support, and resources. Paramedics commonly encounter, either knowingly or unknowingly, patients experiencing IPV. There is little empirical research on how paramedics manage cases involving IPV. OBJECTIVE: To examine how the perspectives and experiences of survivors of IPV who have been attended to by paramedics can inform our understanding of paramedic services. METHODS: Using an interpretive description qualitative approach in the context of paramedicine, self-identified women (18+ years) who reported a history of IPV and being attended to by paramedics for IPV-caused reasons participated in semi-structured interviews. Interviews were transcribed verbatim and de-identified. De-identified transcripts were inductively analyzed (NVivo) for common patterns. RESULTS: N = 9 survivors participated in interviews. Participants experienced cyclic and escalating physical, sexual, psychological, and coercive control forms of IPV. Participants primarily reported accessing paramedic services following instances of severe IPV and reported receiving minimal treatment and support. Challenges included bias and discrimination, poor individual paramedic conduct, undereducated and undertrained paramedics, insufficient infrastructure, inadequate transitions into healthcare and community services, perpetrator dynamics, and survivor dynamics. Corresponding solutions were safe and equitable paramedic behaviour, respectful conduct, mandatory education and training, develop functional infrastructure, develop functional transitions, and utilize techniques to engage with perpetrators and survivors. CONCLUSION: Personal, situational, practitioner, paramedic service, and broad systemic infrastructure challenges cause survivors of IPV to be underserviced by paramedic services. Inadequate intervention efforts may be harmful or fatal for survivors. Survivor-derived solutions may guide paramedic service improvements. With improved service delivery, paramedics could evolve into reliable and useful resources for survivors of IPV.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".