Identifying Factors That Predict Longitudinal Outcomes of Untreated Common Mental Disorders
Bibliographic record
Abstract
OBJECTIVE: Historically, meeting criteria for a mental disorder has been used as a proxy for the need for mental health services, yet research suggests that a significant proportion of disorders remit without treatment. In this study, risk factors for poor longitudinal outcomes of individuals with untreated common mental disorders were determined, with the goal of identifying individuals with unmet need and informing the development of targeted interventions. METHODS: Data came from the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC), a longitudinal, nationally representative survey of the adult U.S. population (age ≥18; N=34,653). Respondents were assessed for past-year depressive, anxiety, and substance use disorders and mental health service use via face-to-face interviews conducted at two time points, three years apart. Among respondents without a history of mental health treatment, logistic regression analyses examined factors associated with persistence of the disorder, comorbidity, or suicide attempt (that is, presence of any axis I disorder in the past year at wave 2 or any suicide attempt during the follow-up) versus spontaneous recovery of baseline disorders. RESULTS: Certain sociodemographic factors, comorbid mental disorders at baseline (such as three or more axis I disorders, adjusted odds ratio [AOR]=1.64, 95% confidence interval [CI]=1.27-2.12), and childhood maltreatment (AOR=1.47, CI=1.23-1.75) were predictors of disorder persistence, comorbidity, or suicide attempt in depressive, anxiety, and substance use disorders during the follow-up. CONCLUSIONS: In addition to considering the presence of a mental disorder, policy makers should consider other variables, such as childhood maltreatment and comorbidity, in estimating treatment need.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".