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Record W2025961650 · doi:10.3402/gha.v7.24000

Medicalization of global health 2: the medicalization of global mental health

2014· article· en· W2025961650 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

VenueGlobal Health Action · 2014
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Toronto
FundersRockefeller Foundation
KeywordsMedicalizationMental healthGlobal mental healthGlobal healthAcknowledgementGlobalizationFraming (construction)PsychiatrySociologyPsychologyMedicinePolitical sciencePublic healthNursingGeographyLaw

Abstract

fetched live from OpenAlex

Once an orphan field, 'global mental health' now has wide acknowledgement and prominence on the global health agenda. Increased recognition draws needed attention to individual suffering and the population impacts, but medicalizing global mental health produces a narrow view of the problems and solutions. Early framing by advocates of the global mental health problem emphasised biological disease, linked psychiatry with neurology, and reinforced categories of mental health disorders. Universality of biomedical concepts across culture is assumed in the globalisation of mental health but is strongly disputed by transcultural psychiatrists and anthropologists. Global mental health movement priorities take an individualised view, emphasising treatment and scale-up and neglecting social and structural determinants of health. To meet international targets and address the problem's broad social and cultural dimensions, the global mental health movement and advocates must develop more comprehensive strategies and include more diverse perspectives.

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)0.002
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: Empirical
Teacher disagreement score0.842
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.032
GPT teacher head0.460
Teacher spread0.428 · 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