Predictors and barriers to minimally adequate treatment among treated individuals with mental disorders: results from the World Mental Health Surveys
Bibliographic record
Abstract
BACKGROUND: Treatments for mental disorders vary widely in type and quality, with many patients failing to receive treatments that meet even minimally adequate standards. We use data from the World Mental Health (WMH) surveys to investigate this variation by examining the prevalence and correlates of minimally adequate treatment (MAT) among patients receiving treatment for common mental disorders. METHODS: Data comes from 25 WMH cross-sectional surveys implemented in 21 countries (n = 1,838 respondents with n = 3,538 12-month treated disorders). MAT was defined according to widely used criteria: pharmacotherapy (≥ 1 month of medication with ≥ four visits to a healthcare provider) or counseling (≥ eight sessions with any provider). Multivariable regression analyses were used to examine associations of socio-demographic, disorder-related, and treatment-related factors with MAT. RESULTS: Approximately two-thirds (66.2%) of treated cases met MAT criteria. There was limited variation in MAT prevalence across disorder types, number of disorders, or years since disorder onset, but MAT prevalence was positively associated with increased disorder severity. Socio-demographic differences were nonsignificant. Relatively substantial differences in MAT prevalence were found by treatment sector (highest MAT prevalence among patients treated by mental health specialists and those treated by multiple provider types). Further analysis showed that these associations were explained by differences in premature discontinuation, completion of a full course of treatment that did not qualify as MAT, and still being in treatment at the time of interview that did not yet qualify as MAT. Low perceived disorder severity unrelated to more objective measures of severity was a central factor in accounting for premature discontinuation. CONCLUSIONS: While approximately two-thirds of treated cases meet MAT criteria, significant gaps remain involving both premature discontinuation and cases where respondents reported completing a 'full recommended course of treatment' that did not involve enough visits or medication duration to meet the MAT standards. Expanding access to mental health specialty providers and increasing patient education about disorder severity would be useful in increasing the proportion of treated cases that receive MAT. Future research should focus on validating MAT definitions against clinical outcomes, standardizing assessment frameworks, and exploring provider- and system-level determinants of treatment adequacy.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".