ORIGINAL ARTICLE Patterns and Predictors of Service Use AmongWomenWho Have Separated from an Abusive Partner
Bibliographic record
Abstract
# The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Using baseline data from a survey of 309 Canadian women recently separated from an abusive partner, we inves-tigated patterns of access to health, social, legal, and violence-specific services and whether abuse history and social and health variables predict service use. We compared rates of service use to population rates, and used logistic regression to identify determinants of use. Service use rates were sub-stantially higher than population estimates in every category, particularly in general and mental health sectors. Although women were confident in their ability to access services, they reported substantial unmet need, difficulty accessing services, and multiple barriers. The strongest unique predictors of use varied across service type. Health variables (high disability chronic pain, symptoms of depression and PTSD), low in-
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".