Predictors of change in mental health and distress among women attending a women's shelter
Bibliographic record
Abstract
Intimate partner violence (IPV) is detrimental to mental health. The Domestic Violence Survivor Assessment (DVSA), which includes a mental health assessment, is often used to evaluate abuse survivors in a counseling situation. The DVSA seeks to outline the cognitive state of women as per the stages of change as they attempt to move toward a life with no IPV. The objective of this study was to explore predictors of change in mental health and distress among women who entered a women's shelter more than once. Women entering a women's shelter more than once over a 3-year period were assessed by a trained social worker using the DVSA. A logistic regression analysis examined relationships between the chosen characteristics and the participants’ mental health through the DVSA stages of change. We analyzed complete data for 94 women who entered the shelter a mean of 3.3 times (range 2–8) over a mean period of 16.1 days (range: 1–391). Thirty-six women (36/94; 38.3%) progressed through the stages. The average number of visits among women who progressed through the stages was 4. Our multivariable logistic regression showed women who had more visits to the shelter were almost twice as likely to progress through the stages compared to women who entered the shelter fewer times (OR=1.928; 95% CI=1.292–2.877; p=0.001). In the univariate analysis, only increased number of visits was significantly associated with progressing through the stages of change (OR=1.694; 95% CI=1.237–2.322; p=0.001). The other factors were not significantly associated with a change in mental health and distress (p>0.05). Women who enter women's shelters more frequently may be more likely to progress through the DVSA mental health stages compared to other women. Women's shelters may be helpful in assisting progression through the stages of change, thereby improving their mental health after abuse.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".