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Record W4353015589 · doi:10.2196/44219

Strengthening Addiction Care Continuum Through Community Consortium in Vietnam: Protocol for a Cluster-Randomized Controlled Trial

2023· article· en· W4353015589 on OpenAlexvenueno aff
Li Li, Tuan Anh Nguyen, Li‐Jung Liang, Chunqing Lin, Phạm Hồng Thắng, Ha Thi Thanh Nguyen, Steven Kha

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Institute of Mental HealthNational Institutes of Health
KeywordsIntervention (counseling)MedicineRandomized controlled trialCommunity healthCluster randomised controlled trialNursingHealth careAllianceAddictionTest (biology)Family medicinePsychologyPublic healthPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: A chronic condition, drug addiction, requires long-term multipronged health care and treatment services. Community-based approaches can offer the advantages of managing integrated care along the care continuum and improving clinical outcomes. However, scant rigorous research focuses on sustainable, community-based care and service delivery. OBJECTIVE: This protocol describes a study aiming to develop and test an intervention that features the alliance of community health workers and family members to provide integrated support and individualized services and treatment for people who use drugs (PWUD) in community settings. METHODS: Based on the National Institute on Drug Abuse's Seek-Test-Treat-Retain (STTR) framework, an intervention that provides training to community health workers will be developed and piloted before an intervention trial. Trained community health workers will conduct home visits and provide support for PWUD and their families. The intervention trial will be conducted in 3 regions in Vietnam, with 60 communities (named communes). These communes will be randomized to either an intervention or control condition. Intervention outcomes will be evaluated at baseline and at 3, 6, 9, and 12 months. The primary outcome measure is PWUD's STTR fulfillment, consisting of multiple individual fulfillment indicators across 5 domains: Seek, Test, Treat, Retain, and Health. The secondary outcomes of interest are the community health workers' service provision and family members' support. The primary analysis will follow an intention-to-treat approach. Generalized mixed-effects regression models will be used to compare changes in the outcome measures from baseline between intervention and control conditions. RESULTS: During the first year of the project, we conducted formative studies, including in-depth interviews and focus groups, to identify service barriers and intervention strategies. The intervention and assessment pilots are scheduled in 2023 before commencing the trial. Reports based on the baseline data will be distributed in early 2024. The intervention outcome results will be available within 6 months of the final data collection date, that is, the main study findings are expected to be available in early 2026. CONCLUSIONS: This study will inform the establishment of community health workers and family members alliance, a locally available infrastructure, to support addiction services and care for PWUD. The methodology, findings, and lessons learned are expected to shed light on the addiction service continuum's implementation and demonstrate a community-based addiction service delivery model that can be transferable to other countries. TRIAL REGISTRATION: ClinicalTrials.gov NCT05315492; https://clinicaltrials.gov/ct2/show/NCT05315492. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/44219.

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)0.047
metaresearch head score (Gemma)0.035
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 score0.098
Threshold uncertainty score0.327

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.035
Meta-epidemiology (narrow)0.0080.004
Meta-epidemiology (broad)0.0170.007
Bibliometrics0.0040.006
Science and technology studies0.0050.005
Scholarly communication0.0060.004
Open science0.0050.003
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0980.013

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 head0.187
GPT teacher head0.547
Teacher spread0.359 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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