Pathway to effective treatment for common mental and substance use disorders in the World Mental Health Surveys: Perceived need for treatment
Bibliographic record
Abstract
BACKGROUND: Perceived need for treatment is a first step along the pathway to effective mental health treatment. Perceived need encompasses a person's recognition that they have a problem and their belief that professional help is needed to manage the problem. These two components could have different predictors. METHODS: Respondents aged 18+ years with 12-month mental disorders from 25 representative household surveys in 21 countries in the World Mental Health Survey Initiative (n = 12,508). All surveys included questions about perceived need; 16 surveys (13 countries) included additional questions about respondents' main reason for perceived need-problem recognition or perceived inability to manage without professional help (n = 9814). Associations of three sets of predictors (disorder, socio-demographics, past treatment) with perceived need and its components were examined using Poisson regression models. RESULTS: Across the 16 surveys with additional questions, 42.4% of respondents with a 12-month mental disorder reported perceived need for treatment. In separate multivariable models for each predictor set: (1) Most disorder types (except alcohol use disorder, specific phobia), disorder severity, and number of disorders were associated with perceived need and both of its components; (2) Sociodemographic factors tended to differentially predict either problem recognition (females, 30-59 years, disabled/unemployed) or need for professional help (females, homemakers, disabled/unemployed, public insurance); (3) Past treatment factors (type of professional, psychotherapy, helpful or unhelpful treatment) were associated with perceived need and both components, except number of past professionals differentially predicted problem recognition. In a consolidated model: employment and insurance became non-significant; type and number of past professionals seen became more important; helpful past treatment predicted greater need for professional help while unhelpful treatment predicted lower problem recognition. Problem recognition was the more important component in determining perceived need for some groups (e.g., severe disorder, people who consulted non-mental health professionals). CONCLUSIONS: Greater clinical need is a key determinant of perceived need for treatment. Findings suggest a need for strategies to address low perceived need (e.g., in males, older people, alcohol use disorders) and lower endorsement of professional treatment in some groups, and to improve patient's treatment experiences which are important enablers of future help-seeking.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".