Child mental health, homelessness, and the shelter system: evidence from Medicaid in New York City
Bibliographic record
Abstract
We identified children who resided in the New York City shelter system during 2015-2020 by matching address histories in Medicaid insurance claims to publicly available homeless shelter addresses, permitting examination of health care use before, during, and after shelter stays. We found that 4.5% of NYC children aged 4-17 with consistent Medicaid coverage entered shelter over a 3-to-5-year period. After shelter entry, children had increased probabilities of receiving mental health services, including therapy and diagnoses of neurodevelopmental disorders but little change in physical health service use. Children placed in shelters colocated with mental health services were similar to children entering other shelters prior to entry but had particularly large and sustained increases in use of mental health services afterwards. Children without prior mental health claims placed in shelters colocated with mental health services were 38%-48% more likely to receive mental health therapy and 14%-16% more likely to receive neurodevelopmental diagnoses than similar children placed elsewhere. These children were also more likely to receive Supplemental Security Income and stayed in shelter longer. This example illustrates the potential of linking administrative data sets in order to study vulnerable populations. This article is part of a Special Collection on Methods in Social Epidemiology.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".