Telemedicine and virtual healthcare for survivors of sexual assault and intimate partner violence: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Survivors of sexual assault and intimate partner violence often face many challenges in seeking/receiving healthcare and are often lost to follow up. OBJECTIVES: Our study objectives are to evaluate the feasibility, acceptability, and satisfaction of using telemedicine technology among sexual assault and intimate partner violence patients who present to a Canadian Emergency Department. DESIGN: Qualitative research was conducted using a thematic approach. METHODS: Patients were identified from a case registry of all sexual assault and intimate partner violence cases seen between 1 April 2020 and 31 March 2022 from an emergency department of a large Canadian hospital. Qualitative trauma-informed interviews were conducted with consenting participants. Thematic qualitative analyses were performed to investigate barriers and drivers of telemedicine for follow-up care. RESULTS: Of the 1007 sexual assault and intimate partner violence patients seen during the study timeframe, 180 (8%) consented to be contacted for future research, and 10 completed an interview regarding telemedicine for follow-up care. All participants were cisgendered women, 5 (50%) experienced sexual assault, 6 (60%) physical assault, and 3 (30%) verbal assault. All knew their assailant, and 6 (60%) were assaulted by a current or former intimate partner. Three themes emerged as drivers of telemedicine use: increased comfort, increased convenience, and less time required for the appointment. Three thematic barriers to telemedicine use included lack of privacy from others, lack of safety from their assailant, and pressure to balance competing tasks during the appointment. CONCLUSION: This study illustrated that telemedicine for sexual assault and intimate partner violence follow-up care is feasible, acceptable, and can improve patient satisfaction with follow-up care. Ensuring safety and privacy are key considerations when offering telemedicine as an appropriate option for survivors.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".