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Record W2899913600 · doi:10.1017/gmh.2018.27

Culture and mental health in Nepal: an interdisciplinary scoping review

2018· article· en· W2899913600 on OpenAlexafffund
Liana Chase, Ram P. Sapkota, Daina Crafa, Laurence J. Kirmayer

Bibliographic record

VenueCambridge Prisms Global Mental Health · 2018
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcGill University
FundersJewish General HospitalMcGill University
KeywordsMental healthPsycINFOGlobal mental healthMental distressMEDLINEMental illnessPsychologyRelevance (law)MedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

Efforts to address global mental health disparities have given new urgency to longstanding debates on the relevance of cultural variations in the experience and expression of distress for the design and delivery of effective services. This scoping review examines available information on culture and mental health in Nepal, a low-income country with a four-decade history of humanitarian mental health intervention. Structured searches were performed using PsycINFO, Web of Science, Medline, and Proquest Dissertation for relevant book chapters, doctoral theses, and journal articles published up to May 2017. A total of 38 publications met inclusion criteria (nine published since 2015). Publications represented a range of disciplines, including anthropology, sociology, cultural psychiatry, and psychology and explored culture in relation to mental health in four broad areas: (1) cultural determinants of mental illness; (2) beliefs and values that shape illness experience, including symptom experience and expression and help-seeking; (3) cultural knowledge of mental health and healing practices; and (4) culturally informed mental health research and service design. The review identified divergent approaches to understanding and addressing mental health problems. Results can inform the development of mental health systems and services in Nepal as well as international efforts to integrate attention to culture in 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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.035
GPT teacher head0.463
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
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

Citations44
Published2018
Admission routes2
Has abstractyes

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