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Record W2768217634 · doi:10.1186/s13033-017-0176-9

Situational analysis to inform development of primary care and community-based mental health services for severe mental disorders in Nepal

2017· article· en· W2768217634 on OpenAlexfundno aff
Mangesh Angdembe, Brandon A. Kohrt, Mark J. D. Jordans, Damodar Rimal, Nagendra P. Luitel

Bibliographic record

VenueInternational Journal of Mental Health Systems · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
FundersNational Institute of Mental HealthGrand Challenges Canada
KeywordsMental healthHealth administrationSituational ethicsPrimary careSituation analysisPrimary health careNursing researchMedicinePsychiatryPublic healthPsychologyNursingFamily medicineEnvironmental healthBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Nepal is representative of Low and Middle Income Countries (LMIC) with limited availability of mental health services in rural areas, in which the majority of the population resides. METHODS: This formative qualitative study explores resources, challenges, and potential barriers to the development and implementation of evidence-based Comprehensive Community-based Mental Health Services (CCMHS) in accordance with the mental health Gap Action Programme (mhGAP) for persons with severe mental health disorders and epilepsy. Focus Group Discussions (FGDs, n = 9) and Key-Informant Interviews (KIIs, n = 26) were conducted in a rural district in western Nepal. Qualitative data were coded using the Framework Analysis Method employing QSR NVIVO software. RESULTS: Health workers, general community members, and persons living with mental illness typically attributed mental illness to witchcraft, curses, and punishment for sinful acts. Persons with mental illness are often physically bound or locked in structures near their homes. Mental health services in medical settings are not available. Traditional healers are often the first treatment of choice. Primary care workers are limited both by lack of knowledge about mental illness and the inability to prescribe psychotropic medication. Health workers supported upgrading their existing knowledge and skills through mhGAP resources. Health workers lacked familiarity with basic computing and mobile technology, but they supported the introduction of mobile technology for delivering effective mental health services. Persons with mental illness and their family members supported the development of patient support groups for collective organization and advocacy. Stakeholders also supported development of focal community resource persons to aid in mental health service delivery and education. CONCLUSION: Health workers, persons living with mental illness and their families, and other stakeholders identified current gaps and barriers related to mental health services. However, respondents were generally supportive in developing community-based care in rural Nepal.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.042
GPT teacher head0.416
Teacher spread0.374 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations39
Published2017
Admission routes1
Has abstractyes

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