Patient-centered substance use disorder treatment for women Veterans
Bibliographic record
Abstract
Introduction: Substance use disorder (SUD) is damaging to women’s health and quality of life. Appropriate treatment can mitigate the effects and health consequences of SUD, yet many woman face access barriers to such treatment. This research seeks to bridge gaps in the current understanding of access to gender-aware care for women Veterans with SUD and to identify ideal treatment program elements for this population. Methods: We interviewed interdisciplinary providers in Los Angeles Veterans Health Administration facilities ( n = 17; psychiatrists, psychologists, social workers, primary care providers, and nurses) and Veterans ( n = 6), identified using purposive snowball sampling, to characterize key components of a non-residential patient-centred SUD treatment program for women Veterans. A semi-structured interview guide elicited current SUD treatment options for women Veterans, barriers to SUD services, and ideal SUD treatment program components. Mutually agreed-on themes were reached using constant comparison. Results: Analyses revealed five key elements of an ideal SUD treatment program for women Veterans: safety (safe and free from harassment in treatment), flexible scheduling (able to accommodate other work and life responsibilities), resourced (no limit to number of visits, staff able to meet needs of comorbidities, on-site child care, etc.), informed providers (providers with access to a comprehensive resource list and aware of easy referral options), and positive (supportive and not punitive). Discussion: The elements identified as necessary for an optimal outpatient SUD treatment program may guide future implementation efforts. SUD programs may not be viable options for women Veterans if they cannot accommodate multidimensional barriers of health care access.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".