“She was Truly an Angel”
Bibliographic record
Abstract
INTRODUCTION: Little is known about the characteristics of women with disabilities who have experienced abuse and their satisfaction with care received from specialized healthcare providers working in hospital-based violence services. METHOD: To address this gap, we surveyed clients presenting to 30 sexual assault/domestic violence treatment centers (SA/DVTCs) in Ontario. RESULTS: Of the 920 women aged 12 years or older who completed a survey, 194 (21%) reported having a disability. Bivariate analyses revealed that women with a disability who experienced abuse were more likely than those without a disability to be older, separated, widowed or divorced, and unemployed; to live alone or to be homeless or living in a shelter; and to report less support from family and friends or colleagues. Women with disabilities were less likely to have been assaulted by acquaintances known for < 24 hours, to be students, and to have been accompanied to the SA/DVTC by another person. Women with disabilities were also more likely than those without disabilities to sustain physical injuries in the assault. Despite these significant differences, almost all women with disabilities rated the care received as excellent or good (97%) and reported that they received the care needed (98%); were able to choose the preferred care (95%); felt safe during the visit (96%); and were treated sensitively (97%), respectfully (96%), and in a nonjudgmental manner (96%). Furthermore, 96% stated that they would recommend the services to others. CONCLUSION: Women with disabilities were overwhelmingly satisfied with SA/DVTC services. However, given their distinct vulnerabilities and increased risk of being injured, attending health providers should receive training relevant to working with this 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".