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Record W3124264690 · doi:10.1002/app5.74

The Political Economy of Mental Health in <scp>V</scp>ietnam: Key Lessons for Countries in Transition

2015· article· en· W3124264690 on OpenAlex
Kelley Lee, Rebecca Zappelli, Elliot M. Goldner, Vu Cong Nguyen, Kitty Corbett, Jill Murphy

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

VenueAsia & the Pacific Policy Studies · 2015
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of WaterlooCentre for Addiction and Mental HealthSimon Fraser University
Fundersnot available
KeywordsMental healthPoliticsTransition (genetics)Economic growthPolitical scienceEconomicsPolitical economyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Among low‐ and middle‐income countries, there is evidence that populations experiencing rapid political and economic transition have particularly high burdens of disease and disability from mental health conditions. This paper undertakes a political economy analysis of mental health in Vietnam to enhance knowledge translation, notably how both explicit and tacit knowledge can be used to promote evidence‐based policy making. It argues that Vietnam's experience illustrates the need to better understand, not only how transition transforms societies, but how it impacts on the mental health needs and care of populations. The political economy of transition in Vietnam has so far given highest priority to economic growth through integration with the world economy and public sector reform. There is a need to recognise that transition in Vietnam poses both a potential threat to the care of people with mental health needs, and an opportunity to develop mental health services appropriate to local contexts.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.852
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.096
GPT teacher head0.438
Teacher spread0.342 · 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