Bibliographic record
Abstract
This study reports a national survey of U.S. states that was conducted from July of 1999 through March of 2001. The lack of consistent data on serious mental illness (SMI) provided the impetus for this study. Data was collected through a survey on states' definitions of SMI, on demographic information for patients with SMI, and on total annual per capita expenditures for SMI. Based on a 100% response rate, we found considerable variation among states in the definition used for SMI and the records kept on patients with SMI. This paper also involves a state-level statistical analysis of factors that may influence rates of per capita expenditures for SMI. The main finding using regression analysis was that per capita income and state definitions of mental illness that included DSM-III, DSM-IV, and ICD-9-CM diagnoses are significant and positively associated with a state's per capita expenditures for SMI. An additional finding is that accounting for all of the above factors, there still remains significant differences across major census divisions in per capita expenditures for the seriously mentally ill. Another major finding is that more consistent data collection is needed to take an epidemiological approach toward understanding the social conditions that contribute to SMI.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".