Barriers to 12-month treatment of common anxiety, mood, and substance use disorders in the World Mental Health (WMH) surveys
Bibliographic record
Abstract
BACKGROUND: High unmet need for treatment of mental disorders exists throughout the world. An understanding of barriers to treatment is needed to develop effective programs to address this problem. METHODS: Data on barriers were obtained from face-to-face interviews in 22 community surveys across 19 countries (n = 102,812 respondents aged ≥ 18 years, 57.7% female, median age [interquartile range]: 43 [31-57] years; 68.5% weighted average response rate) in the World Mental Health (WMH) surveys. We focus on the n = 5,136 respondents with 12-month DSM-IV anxiety, mood, or substance use disorders with perceived need for treatment. The n = 2,444 such respondents who did not receive treatment were asked about barriers to receiving treatment, whereas the n = 926 respondents who received treatment with a delay were asked about barriers leading to delays. Consistent with previous research, we distinguished five broad classes of barriers: low perceived disorder severity, two types of barriers in the domain of predisposing factors (beliefs/attitudes about treatment ineffectiveness and stigma) and two types in the domain of enabling factors (financial and nonfinancial). Baseline predictors of receiving treatment found in a prior report (i.e., comparing the n = 2,692 respondents who received treatment with the n = 2,444 who did not) were examined as predictors of barriers, while barriers were examined as mediators of associations between these predictors and treatment. RESULTS: = 3.8-199.8, p = 0.050- < 0.001). Barriers were predicted by low education, disorder type, age, employment status, and financial obstacles. Predictors varied as a function of barrier type. CONCLUSIONS: A wide range of barriers to treatment exist among people with mental disorders even after a need for treatment is acknowledged. Most such individuals have multiple barriers. These results have important implications for the design of programs to decrease unmet need for treatment of mental disorders.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".