The prevalence and correlates of untreated serious mental illness.
Bibliographic record
Abstract
OBJECTIVE: To identify the number of people in the United States with untreated serious mental illness (SMI) and the reasons for their lack of treatment. DATA SOURCE/STUDY DESIGN: The National Comorbidity Survey; cross-sectional, nationally representative household survey. DATA COLLECTION: An operationalization of the SMI definition set forth in the Alcohol, Drug Abuse, and Mental Health Administration Reorganization Act identified individuals with SMI in the 12 months prior to the interview. The presence of SMI then was related to the use of mental health services in the past 12 months. PRINCIPAL FINDINGS: Of the 6.2 percent of respondents who had SMI in the year prior to interview, fewer than 40 percent received stable treatment. Young adults and those living in nonrural areas were more likely to have unmet needs for treatment. The majority of those who received no treatment felt that they did not have an emotional problem requiring treatment. Among those who did recognize this need, 52 percent reported situational barriers, 46 percent reported financial barriers, and 45 percent reported perceived lack of effectiveness as reasons for not seeking treatment. The most commonly reported reason both for failing to seek treatment (72 percent) and for treatment dropout (58 percent) was wanting to solve the problem on their own. CONCLUSIONS: Although changes in the financing of services are important, they are unlikely by themselves to eradicate unmet need for treatment of SMI. Efforts to increase both self-recognition of need for treatment and the patient centeredness of care also are needed.
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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".