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Record W4380576127 · doi:10.2196/46486

Assessing the Feasibility of Economic Approaches to Prevent Substance Abuse Among Adolescents: Protocol for a Mixed Methods Study

2023· article· en· W4380576127 on OpenAlexvenueno aff
Rachel Brathwaite, Massy Mutumba, Jacqueline Nanteza, Lindsey M. Filiatreau, Herbert Migadde, Phionah Namatovu, Betina Nabisere, James Mugisha, Abel Mwebembezi, Fred M. Ssewamala

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Institute of Mental HealthNational Institute on Alcohol Abuse and Alcoholism
KeywordsProtocol (science)Substance abusePsychologyMedicineEnvironmental healthPsychiatryAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Adolescent alcohol and drug use (ADU) is a significant public health challenge. Uganda, one of the poorest countries in Sub-Saharan Africa (SSA), has the second-highest rate of per capita alcohol consumption in SSA, and over one-third of Ugandan adolescents have used alcohol in their lifetime (over 50% of them engage in heavy episodic drinking). These estimates further increase in fishing villages, a key HIV-vulnerable population, where ADU is normative. However, few studies have assessed ADU among adolescents and youths living with HIV despite their increased risk for ADU and its impact on engagement in HIV care. Moreover, data on risk and resilience factors for ADU are scarce as only few studies evaluating ADU interventions in SSA have reported positive outcomes. The majority have been implemented in school settings, potentially excluding adolescents in fishing communities with high school dropout rates, and none have targeted risk factors including poverty and mental health, which are rampant among adolescents and youths living with HIV and their families, undermine their coping skills and resources, and have been associated with increased risk for ADU among them. OBJECTIVE: We propose a mixed methods study with a sample of 200 adolescents and youths living with HIV (aged 18-24 years) seen at 6 HIV clinics in southwestern Uganda's fishing communities to (1) examine the prevalence and consequences of ADU and identify the multilevel risk and resilience factors associated with ADU among them and (2) explore the feasibility and short-term effects of an economic empowerment intervention on ADU among them. METHODS: This study comprises four components: (1) focus group discussions (FGDs) with adolescents and youths living with HIV (n=20) and in-depth qualitative interviews with health providers (n=10) from 2 randomly selected clinics; (2) a cross-sectional survey with 200 adolescents and youths living with HIV; (3) a randomized controlled trial with a subgroup of adolescents and youths living with HIV (n=100); and (4) 2 postintervention FGD with adolescents and youths living with HIV (n=10 per group). RESULTS: Participant recruitment for the first qualitative phase has completed. As of May 4, 2023, ten health providers from 6 clinics have been recruited, provided written consent to participate, and participated in in-depth qualitative interviews. Two FGDs was conducted with 20 adolescents and youths living with HIV from 2 clinics. Data transcription, translation, and analysis of qualitative data has commenced. The cross-sectional survey will commence shortly after and dissemination of the main study findings is targeted for 2024. CONCLUSIONS: Our findings will advance our understanding of ADU among adolescents and youths living with HIV and inform the design of future interventions to address ADU among them. TRIAL REGISTRATION: ClinicalTrials.gov NCT05597865; https://clinicaltrials.gov/ct2/show/NCT05597865. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46486.

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)0.069
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.069
Threshold uncertainty score0.363

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.049
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0040.004
Science and technology studies0.0050.003
Scholarly communication0.0040.003
Open science0.0050.004
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0590.009

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 head0.853
GPT teacher head0.731
Teacher spread0.122 · 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 designNot applicable
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

Citations4
Published2023
Admission routes1
Has abstractyes

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