MétaCan
Menu
Back to cohort
Record W3006710561 · doi:10.1177/1363461519898035

Global Mental Health: Interdisciplinary challenges for a field in motion

2020· article· en· W3006710561 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTranscultural Psychiatry · 2020
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcGill University
Fundersnot available
KeywordsMental healthPsychological interventionGlobal mental healthReflexivitySociologyEngineering ethicsConstruct (python library)InterdisciplinarityField (mathematics)PsychologySocial sciencePsychotherapistComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

In recent years, efforts in Global Mental Health (GMH) have evolved alongside critical engagement with the field's claims and interventions. GMH has shifted its agenda and epistemological underpinnings, increased its evidence base, and joined other global policy platforms such as the Sustainable Development Goals. This editorial introduction to a thematic issue traces the recent shifts in the GMH agenda and discusses the changing construct of “mental health” as GMH moves away from a categorical biomedical model toward dimensional and transdiagnostic approaches and embraces digital technologies. We highlight persistent and emerging lines of inquiry and advocate for meaningful interdisciplinary engagement. Taken together, the articles in this special issue of Transcultural Psychiatry provide a snapshot of current interdisciplinary work in GMH that considers the socio-cultural and historical dimensions of mental health important and proposes reflexive development of interventions and implementation strategies.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.795
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.416
Teacher spread0.344 · 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