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Record W2162361106 · doi:10.3402/gha.v7.24589

Declaration on mental health in Africa: moving to implementation

2014· article· en· W2162361106 on OpenAlexaff
Abdallah S. Daar, Marian Jacobs, Stig Wall, Johann Groenewald, Julian Eaton, Vikram Patel, Palmira dos Santos, Ashraf Kagee, Aní­k Gevers, Charlene Sunkel, Gail Andrews, Ingrid Daniels, David M. Ndetei

Bibliographic record

VenueGlobal Health Action · 2014
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMental Health Research CanadaUniversity of Toronto
FundersWellcome Trust
KeywordsMental healthDeclarationAction planHealth policyGlobal healthMedicineEconomic growthInclusion (mineral)Mental health lawStigma (botany)Global mental healthHealth careHuman rightsHealth promotionPolitical scienceNursingPsychiatryPublic healthPsychology

Abstract

fetched live from OpenAlex

Urgent action is needed to address mental health issues globally. In Africa, where mental health disorders account for a huge burden of disease and disability, and where in general less than 1% of the already small health budgets are spent on these disorders, the need for action is acute and urgent. Members of the World Health Organization, including African countries, have adopted a Comprehensive Mental Health Action Plan. Africa now has an historic opportunity to improve the mental health and wellbeing of its citizens, beginning with provision of basic mental health services and development of national mental health strategic plans (roadmaps). There is need to integrate mental health into primary health care and address stigma and violations of human rights. We advocate for inclusion of mental health into the post-2015 Sustainable Development Goals, and for the convening of a special UN General Assembly High Level Meeting on Mental Health within three years.

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)0.035
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.185

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.051
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0050.006
Scholarly communication0.0090.007
Open science0.0060.018
Research integrity0.0270.038
Insufficient payload (model declined to judge)0.0140.005

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 head0.090
GPT teacher head0.491
Teacher spread0.402 · 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 designNot applicable
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

Citations36
Published2014
Admission routes1
Has abstractyes

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