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Record W4407280991 · doi:10.1186/s13033-024-00658-2

Barriers to 12-month treatment of common anxiety, mood, and substance use disorders in the World Mental Health (WMH) surveys

2025· article· en· W4407280991 on OpenAlexafffund
María Carmen Viana, Alan E. Kazdin, Meredith Harris, Dan J. Stein, Daniel Vigo, Irving Hwang, Nancy A. Sampson, Jordi Alonso, Laura Helena Andrade, Guilherme Borges, Brendan Bunting, José Miguel Caldas‐de‐Almeida, Giovanni de Girolamo, Peter de Jonge, Oye Gureje, Josep María Haro, Elie G. Karam, Viviane Kovess–Masféty, Jacek Moskalewicz, Fernando Navarro‐Mateu, Daisuke Nishi, Marina Piazza, José Posada‐Villa, Kate M. Scott, Cristian Vlădescu, Bogdan Wojtyniak, Zahari Zarkov, Ronald C. Kessler, Timothy L. Kessler, Sergio Aguilar‐Gaxiola, Yasmin Altwaijri, Lukoye Atwoli, Corina Benjet, Evelyn J. Bromet, Ronny Bruffaerts, Jose Miguel Caldas-de-Almeida, Graça Cardoso, Stephanie Chardoul, Alfredo H. Cía, Louisa Degenhardt, Ma. Lourdes Rosanna E. de Guzman, Hristo Hinkov, Chiyi Hu, Aimée Karam, Georges Karam, Norito Kawakami, Salma M. Khaled, Andrzej Kiejna, John J. McGrath, María Elena Medina-Mora, Juan Carlos Stagnaro, Margreet ten Have, Yolanda Torres, David R. Williams, Peter Woodruff, Miguel Xavier, Alan M. Zaslavsky

Bibliographic record

VenueInternational Journal of Mental Health Systems · 2025
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of British Columbia Hospital
FundersOrtho-McNeil PharmaceuticalCanadian Institutes of Health ResearchHealth CanadaPan American Health OrganizationPfizer FoundationU.S. Public Health ServiceNational Institute on Drug AbuseSubstance Abuse and Mental Health Services AdministrationEli Lilly and CompanyJohn D. and Catherine T. MacArthur FoundationProvincial Health Services AuthoritySouth African Medical Research CouncilUniversity of British ColumbiaFogarty International CenterNational Institute of Mental HealthJohn W. Alden TrustRobert Wood Johnson Foundation
KeywordsAnxietyMental healthPsychiatryMoodHealth administrationSubstance useMood disordersPsychologyClinical psychologyMedicinePublic healthNursing

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.403
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2025
Admission routes2
Has abstractyes

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