Variations in depression care and outcomes among high-risk mothers from different racial/ethnic groups
Bibliographic record
Abstract
UNLABELLED: PURPOSE. To examine variations in depression care and outcomes among high-risk pregnant and parenting women from different racial/ethnic groups served in community health centres. METHODS: As part of a collaborative care programme that provides depression treatment in primary care clinics for high-risk mothers, 661 women with probable depression (Patient Health Questionnaire-9 ≥ 10), who self-reported race/ethnicity as Latina (n = 393), White (n = 126), Black (n = 75) or Asian (n = 67), were included in the study. Primary outcomes include quality of depression care and improvement in depression. A Cox proportional hazard model adjusting for sociodemographic and clinical characteristics was used to examine time to treatment response. RESULTS: We observed significant differences in both depression processes and outcomes across ethnic groups. After adjusting for other variables, Blacks were found to be significantly less likely to improve than Latinas [hazard ratio (HR): 0.53, 95% confidence interval (CI): 0.44-0.65]. Other factors significantly associated with depression improvement were pregnancy (HR: 1.52, 95% CI: 1.27-1.82), number of clinic visits (HR: 1.26, 95% CI: 1.17-1.36) and phone contacts (HR: 1.45, 95% CI: 1.32-1.60) by the care manager in the first month of treatment. After controlling for depression severity, having suicidal thoughts at baseline was significantly associated with a decreased likelihood of depression improvement (HR: 0.75, 95% CI: 0.67-0.83). CONCLUSIONS: In this racially and ethnically diverse sample of pregnant and parenting women treated for depression in primary care, the intensity of care management was positively associated with improved depression. There was also appreciable variation in depression outcomes between Latina and Black patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".