Patterns of depressive symptoms and antidepressant use among women survivors of intimate partner violence
Bibliographic record
Abstract
Abstract\nPurpose\nOne of the primary mental health responses of women experiencing intimate partner violence (IPV) is depression, yet little is known about the mental health and antidepressant use of women in the period after leaving an abusive partner. We investigate patterns of antidepressant use and depressive symptoms by various social indicators (parenting status, socioeconomic status, severity of abuse and disclosure of abuse). Second, we examine whether variation in antidepressant use is explained by higher rates of depression diagnoses and/or depressive symptoms, taking these social indicators into consideration.\nMethods\nWe examine data from the Women’s Health Effects Study, a community sample of 309 Canadian women who have recently left an abusive partner.\nResults\nBivariate results reveal that over 80% of women with elevated depressive symptoms are without diagnosis and antidepressant medication. Multivariate analyses show that antidepressant use is predicted by an indicator of economic disadvantage, with women who receive social assistance or disability benefits being more likely to report elevated antidepressant use, controlling for both depressive symptoms and depression diagnoses.\nConclusions\nDocumenting and explaining depressive symptoms and antidepressant use among IPV survivors provides insight into one of many possible treatment options available to women with depression, and sheds light on potential health disparities among this subgroup of the 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".