Financial Barriers to Mental Healthcare Services and Depressive Symptoms among Residents of Washington Heights, New York City
Bibliographic record
Abstract
Objectives: In the United States, Hispanics are more likely to experience financial barriers to mental health care than non-Hispanics. We used a unique survey to study the effect of these financial barriers on the severity of depressive symptoms among Hispanics who had previously been diagnosed as having depression. Methods: This cross-sectional study used data from the 2015 Washington Heights Community Survey, administered to 2,489 households in Manhattan, New York City. Multiple regression models and propensity score matching were used to estimate the association between financial barriers to mental health care and depressive symptoms and the likelihood of being clinically depressed. Results: Among those diagnosed with depression, those with financial barriers to mental health services or counseling had significantly higher (β = 0.36, 95% CI = 0.03, 0.70) depressive symptoms. When propensity score matching was utilized, those with financial barriers to mental health services had significantly greater depressive symptoms (β = 0.63, 95% CI = 0.37, 0.89) and were significantly more likely to be currently depressed (OR = 2.38, 95% CI = 1.46, 3.89), in comparison to those who had access. Conclusions: Making mental health care more affordable and therefore more accessible to Hispanics is one step toward mitigating the burden on mental illness and decreasing health disparities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".