MétaCan
Menu
Back to cohort
Record W3133468218 · doi:10.1111/bdi.13066

Perceived helpfulness of bipolar disorder treatment: Findings from the World Health Organization World Mental Health Surveys

2021· article· en· W3133468218 on OpenAlexaff
Andrew A. Nierenberg, Meredith Harris, Alan E. Kazdin, Victor Puac‐Polanco, Nancy A. Sampson, Daniel Vigo, Wai Tat Chiu, Hannah N. Ziobrowski, Jordi Alonso, Yasmin Altwaijri, Guilherme Borges, Brendan Bunting, José Miguel Caldas‐de‐Almeida, Josep María Haro, Chi‐yi Hu, Andrzej Kiejna, Sing Lee, John J. McGrath, Fernando Navarro‐Mateu, José Posada‐Villa, Kate M. Scott, Juan Carlos Stagnaro, María Carmen Viana, Ronald C. Kessler

Bibliographic record

VenueBipolar Disorders · 2021
Typearticle
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of British Columbia
FundersFogarty International CenterNational Institute on Drug AbuseNational Institute of Mental HealthFundação para a Ciência e a TecnologiaAstraZenecaPfizer FoundationServierSaudi Basic Industries CorporationJohn D. and Catherine T. MacArthur FoundationEli Lilly and CompanyU.S. Public Health ServiceAlcohol Advisory Council of New ZealandPan American Health OrganizationNovartisGlaxoSmithKlineCalouste Gulbenkian FoundationSubstance Abuse and Mental Health Services AdministrationMinisterio de Salud de la NaciónMinistry of Health, New ZealandBristol-Myers SquibbKing Saud UniversityJohn W. Alden TrustPfizerHealth Research Council of New ZealandDepartment of Health and Ageing, Australian GovernmentKing Abdulaziz City for Science and TechnologyRobert Wood Johnson Foundation
KeywordsHelpfulnessBipolar disorderMental healthPsychiatryPsychologyTreatment of bipolar disorderClinical psychologyManiaSocial psychologyMood

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine patterns and predictors of perceived treatment helpfulness for mania/hypomania and associated depression in the WHO World Mental Health Surveys. METHODS: Face-to-face interviews with community samples across 15 countries found n = 2,178 who received lifetime mania/hypomania treatment and n = 624 with lifetime mania/hypomania who received lifetime major depression treatment. These respondents were asked whether treatment was ever helpful and, if so, the number of professionals seen before receiving helpful treatment. Patterns and predictors of treatment helpfulness were examined separately for mania/hypomania and depression. RESULTS: 63.1% (mania/hypomania) and 65.1% (depression) of patients reported ever receiving helpful treatment. However, only 24.5-22.5% were helped by the first professional seen, which means that the others needed to persist in help seeking after initial unhelpful treatments in order to find helpful treatment. Projections find only 22.9% (mania/hypomania) and 43.3% (depression) would persist through a series of unhelpful treatments but that the proportion helped would increase substantially if persistence increased. Few patient-level significant predictors of helpful treatment emerged and none consistently either across the two components (i.e., provider-level helpfulness and persistence after earlier unhelpful treatment) or for both mania/hypomania and depression. Although prevalence of treatment was higher in high-income than low/middle-income countries, proportional helpfulness among treated cases was nearly identical in the two groups of countries. CONCLUSIONS: Probability of patients with mania/hypomania and associated depression obtaining helpful treatment might increase substantially if persistence in help-seeking increased after initially unhelpful treatments, although this could require seeing numerous additional treatment providers. In addition to investigating reasons for initial treatments not being helpful, messages reinforcing the importance of persistence should be emphasized to patients.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.272
Teacher spread0.257 · 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

Citations11
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBipolar DisordersSame topicBipolar Disorder and TreatmentFrench-language works237,207