MétaCan
Menu
Back to cohort
Record W2786948863 · doi:10.1186/s40814-018-0234-3

Reducing stigma among healthcare providers to improve mental health services (RESHAPE): protocol for a pilot cluster randomized controlled trial of a stigma reduction intervention for training primary healthcare workers in Nepal

2018· article· en· W2786948863 on OpenAlexafffund
Brandon A. Kohrt, Mark J. D. Jordans, Elizabeth L. Turner, Kathleen J. Sikkema, Nagendra P. Luitel, Sauharda Rai, Daisy R. Singla, Jagannath Lamichhane, Crick Lund, Vikram Patel

Bibliographic record

VenuePilot and Feasibility Studies · 2018
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsSinai Health SystemUniversity of Toronto
FundersNational Institute of Mental HealthDepartment for International Development, UK GovernmentNational Institutes of HealthUniversity of TorontoDepartment for International DevelopmentMedical Psychiatry Alliance
KeywordsStigma (botany)Mental healthHealth careRandomized controlled trialProtocol (science)Intervention (counseling)MedicineNursingMental healthcareHealthcare workerPsychiatryPsychologyFamily medicineAlternative medicinePolitical science

Abstract

fetched live from OpenAlex

Non-specialist healthcare providers, including primary and community healthcare workers, in low- and middle-income countries can effectively treat mental illness. However, scaling-up mental health services within existing health systems has been limited by barriers such as stigma against people with mental illness. Therefore, interventions are needed to address attitudes and behaviors among non-specialists. Aimed at addressing this gap, REducing Stigma among HealthcAre Providers to ImprovE mental health services (RESHAPE) is an intervention in which social contact with mental health service users is added to training for non-specialist healthcare workers integrating mental health services into primary healthcare. This protocol describes a mixed methods pilot and feasibility study in primary care centers in Chitwan, Nepal. The qualitative component will include key informant interviews and focus group discussions. The quantitative component consists of a pilot cluster randomized controlled trial (c-RCT), which will establish parameters for a future effectiveness study of RESHAPE compared to training as usual (TAU). Primary healthcare facilities (the cluster unit, k = 34) will be randomized to TAU or RESHAPE. The direct beneficiaries of the intervention are the primary healthcare workers in the facilities (n = 150); indirect beneficiaries are their patients (n = 100). The TAU condition is existing mental health training and supervision for primary healthcare workers delivered through the Programme for Improving Mental healthcarE (PRIME) implementing the mental health Gap Action Programme (mhGAP). The primary objective is to evaluate acceptability and feasibility through qualitative interviews with primary healthcare workers, trainers, and mental health service users. The secondary objective is to collect quantitative information on health worker outcomes including mental health stigma (Social Distance Scale), clinical knowledge (mhGAP), clinical competency (ENhancing Assessment of Common Therapeutic factors, ENACT), and implicit attitudes (Implicit Association Test, IAT), and patient outcomes including stigma-related barriers to care, daily functioning, and symptoms. The pilot and feasibility study will contribute to refining recommendations for implementation of mhGAP and other mental health services in primary healthcare settings in low-resource health systems. The pilot c-RCT findings will inform an effectiveness trial of RESHAPE to advance the evidence-base for optimal approaches to training and supervision for non-specialist providers. ClinicalTrials.gov identifier, NCT02793271

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.063
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.021
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0030.003
Science and technology studies0.0040.004
Scholarly communication0.0040.004
Open science0.0040.003
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0630.009

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 head0.180
GPT teacher head0.475
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

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

Citations136
Published2018
Admission routes2
Has abstractyes

Explore more

Same venuePilot and Feasibility StudiesSame topicMental Health Treatment and AccessFrench-language works237,207