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Record W1924564109 · doi:10.1176/appi.ps.201500066

Mental Health Care for Vulnerable People With Complex Needs in Low-Income Countries: Two Services in West Africa

2015· article· en· W1924564109 on OpenAlexaff
Julian Eaton, Benoît des Roches, Kenneth Nwaubani, Lopa Winters

Bibliographic record

VenuePsychiatric Services · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCanadian Association of Emergency Physicians
Fundersnot available
KeywordsMental healthFocus groupMental illnessEconomic growthScale (ratio)Low and middle income countriesNursingDeveloping countryHealth careMedicineBusinessPsychologyPsychiatryGeographyEconomicsMarketing

Abstract

fetched live from OpenAlex

People with severe and enduring mental illnesses, such as schizophrenia, are among the most disabled, socially excluded, and underserved populations, especially in low- and middle-income countries. Some programs have been created to target this group. The current global development agenda emphasizes the need to provide care to vulnerable groups. This column compares two long-standing and successful programs for homeless people with mental illness in three West African countries--Nigeria, Côte d'Ivoire, and Bénin. The authors describe essential ingredients of these programs and their integration into existing systems, including funding and other resources, leadership models, and staff. The success of these programs provides support for initiatives to scale up services for people with severely disabling and complex needs, even as the focus is increasingly on cost-effectiveness of mental health integration into decentralized health services.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.374
Teacher spread0.349 · 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

Citations55
Published2015
Admission routes1
Has abstractyes

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