Approaches to Women's Health and Survivors of Domestic Violence in Canada
Bibliographic record
Abstract
We are in the process of developing guidelines about supporting domestic violence survivors for health care providers and institutions based on research evidence. In this report we describe what we gained from attending a six-day program on this topic at the Children's & Women's Health Centre of British Columbia (B.C. Women's), a large and prestigious institution for women's health care. Our purpose in attending was to gain information related to medical, nursing and social welfare activities for survivors of domestic violence, to get suggestions about making guidelines for such activities and to make contact with other participants from Japan. B.C. Women's is guided by the principle that it ought to provide 'woman centered care', and, in doing so, respect women and ensure their safety. B.C. Women's provides high quality health services for women and infants during the peri-natal period, genetic counseling, and a broad range of specialized women's health services. At B.C. Women's there are collaborative relationships among the clinical, research, and teaching staff. In interviews, program participants suggested that it was important to provide sensitive care at hospitals based on the principle of woman centered care, educate and train all staff, and built networks with other care providers and organizations in the community who are interested in domestic violence and the well-beine of women and families.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".