Barriers to Accessing Services for Mental Health, Domestic Violence, and Poverty in New York City: A Mixed-Methods Ecological Study
Bibliographic record
Abstract
Across New York City, millions struggle to access support for mental health concerns, domestic violence and/or for poverty—often when they need these services the most. Yet, little research captures the voices of those most affected. This mixed-methods study, supported by local nonprofit Believe New York, sought to identify: (1) individual-level characteristics associated with barriers to help-seeking, and (2) perceived barriers at personal, interpersonal, and structural levels. A total of 736 adults with lived experience of poverty, domestic or intimate partner violence, or mental health challenges completed a survey available in commonly spoken languages in NYC (i.e., English, Mandarin, and Spanish) between November 2023 and August 2024. Participants were recruited through in-person outreach at community events and targeted online efforts. Quantitative analysis revealed that ethnic minorities, non-native English speakers, and those with financial insecurity were more likely to report difficulty accessing or trusting services. Thematic analysis of 448 open-ended responses uncovered widespread emotional, practical, interpersonal, and structural barriers. Findings underscore the urgency of developing trauma-informed, culturally responsive, and family-inclusive programs and services. Future initiatives and social service providers should incorporate public de-stigmatization campaigns, expand service availability, and actively engage marginalized communities in their unique needs and concerns.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".