Client Satisfaction With Nursing-led Sexual Assault and Domestic Violence Services in Ontario
Bibliographic record
Abstract
INTRODUCTION: There is still little known about survivors' experiences of and satisfaction with comprehensive nursing-led hospital-based sexual assault and domestic violence treatment programs. METHOD: To address this gap, we surveyed and collected information from clients/guardians presenting to 30 of 35 of Ontario's Sexual Assault/Domestic Violence Treatment Centres across seven domains: presentation characteristics, client characteristics, assailant characteristics, assault characteristics, health consequences, service use, and satisfaction with services. RESULTS: One thousand four hundred eighty-four clients participated in the study, 96% of whom were women/girls. Most were White (75.3%), 12-44 years old (87.8%), and living with family (69.6%); 97.9% of clients used at least one service. The most commonly used service was assessment and/or documentation of injury (84.8%), followed by on-site follow-up care (73.6%). Almost all clients/guardians reported that they received the care needed (98.6%), rated the overall care as excellent or good (98.8%), and stated that the care had been provided in a sensitive manner (95.4%). Concerns and recommendations to improve care expressed by a small proportion of clients/guardians focused on long wait times, negative emergency department staff attitudes, issues of privacy and confidentiality, and difficulty with accessing services. DISCUSSION: The high uptake and positive evaluation of services provided by Ontario's Sexual Assault/Domestic Violence Treatment Centre programs confirms the value of nursing-led, hospital-based care in the aftermath of sexual assault and domestic violence. Ongoing evaluation of such services will ensure the best care possible for this patient population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".