Income Differences in Persons Seeking Outpatient Treatment for Mental Disorders
Bibliographic record
Abstract
BACKGROUND: Variations in the relationships among income, use of mental health services, and sector of care are examined by comparing data from 3 countries that differ in the organization and financing of mental health services. METHODS: Data come from the 1990-1992 National Comorbidity Survey (n = 5,384), the 1990-1991 Mental Health Supplement to the Ontario Health Survey (n = 6,321), and the 1996 Netherlands Mental Health Survey and Incidence Study (n = 6031). Analysis of the association between income and use of mental health services was carried out for the population that was between ages 18 and 54 years. Differential use of mental health treatment was examined in 3 sectors: the general medical sector, the specialty sector, and the human services sector. RESULTS: No significant association between income and probability of any mental health treatment was observed for persons with psychiatric disorders in any of the 3 countries. However, there were significant differences among countries in the association between income and sector of mental health care treatment. In the United States, income is positively related to treatment being received in the specialty sector and negatively related to treatment being received in the human services sector. In the Netherlands, patients in the middle-income bracket are less likely to receive specialty care, while those in the high-income bracket are less likely to be seen in the human service sector. Income is unrelated to the sector of care for patients in Ontario. CONCLUSIONS: Future research should examine whether differential access to the specialty sector for low-income people in the United States is associated with worse mental health outcomes.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".