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Record W4205662126 · doi:10.3389/fpsyt.2021.822299

Editorial: The Nine Grand Challenges in Global Mental Health

2022· editorial· en· W4205662126 on OpenAlexaff
Malek Bajbouj, Thi Minh Tam Ta, Ghayda Hassan, Eric Hahn

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typeeditorial
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsGrand ChallengesMental healthGlobal mental healthPsychologyPsychiatryMedicinePolitical science

Abstract

fetched live from OpenAlex

Global Mental Health in Times of Pandemic and MigrationGrand global challenges significantly impact mental health and well-being in vulnerable populations across the globe.Today, national health systems and infrastructures are often not sufficiently equipped to react effectively to these grand challenges' multiple and interrelated consequences.The World Health Organization has identified 13 urgent health challenges for the upcoming decade (see Table 1).From our perspective, nine of these challenges are crucial not only for global health but also for Global Mental Health.

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)0.030
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.030
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0040.002
Science and technology studies0.0060.003
Scholarly communication0.0100.007
Open science0.0050.002
Research integrity0.0190.024
Insufficient payload (model declined to judge)0.0240.023

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 head0.016
GPT teacher head0.347
Teacher spread0.331 · 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
GenreEditorial

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
Published2022
Admission routes1
Has abstractyes

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