Newcomers and Old Timers: An Erroneous Assumption in Mental Health Services Research
Bibliographic record
Abstract
Based on the premise that treatment changes people in ways that are consequential for subsequent treatment-seeking, we question the validity of an unrecognized and apparently inadvertent assumption in mental health services research conducted within a psychiatric epidemiology paradigm. This homogeneity assumption statistically constrains the effects of potential determinants of recent treatment to be identical for former patients and previously untreated persons by omitting treatment history or modeling only main effects. We test this assumption with data from the 2001–2003 Collaborative Psychiatric Epidemiology Surveys; the weighted pooled sample is representative of noninstitutionalized U.S. adults (18+; analytic n = 19,227). Contrary to the homogeneity assumption, some associations with recent treatment are conditional on past treatment, including psychiatric disorder and race-ethnicity—measures of need and treatment disparities, respectively. We conclude that the widespread application of the homogeneity assumption probably masks differences in the determinants of recent use between previously untreated persons and former patients.
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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 | 0.000 | 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) | 0.000 | 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".