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Record W3164525356 · doi:10.2196/29427

Understanding Neighborhoods’ Impact on Youth Substance Use and Mental Health Outcomes in Paterson, New Jersey: Protocol for a Community-Based Participatory Research Study

2021· article· en· W3164525356 on OpenAlexvenueno aff
Ijeoma Opara, Noelle R. Leonard, Daneele Thorpe, Trace Kershaw

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Center for Advancing Translational SciencesNational Institute of Mental HealthNational Institutes of Health
KeywordsPsychological interventionParticipatory action researchFocus groupMental healthCommunity-based participatory researchPsychologySubstance usePublic healthSubstance abuseQualitative researchIntervention (counseling)Vulnerability (computing)Qualitative propertyEnvironmental healthGerontologyMedicineClinical psychologyPsychiatrySociologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Substance use among youth is a major public health concern. Of note, substance use among youth is increasing in prevalence, and the incidence of substance use at earlier ages is rising. Given the long-term consequences of early substance use, it is important to identify factors that increase youth vulnerability to drug use, as they may be important targets for future interventions. OBJECTIVE: This study aims to use innovative methods, such as venue-based sampling, to recruit youth who are disconnected from school and use community-based participatory research to gain a better understanding of the prevalence of substance use and important correlates among youth aged between 13 and 21 years in Paterson, New Jersey, a low-income, urban community. The study will use a convergent, mixed methods design involving multiple data collection components and the analysis of a ministrative data source, designed with the strengths of complex intervention frameworks in mind. The overall aims of the study are to identify the prevalence of substance use among youth who are engaged in school and not engaged in school; to understand important antecedents and correlates of substance use; and to use this information to inform social, environmental, and culturally appropriate interventions to address substance use and its correlates among youths in a lower-resourced urban community. METHODS: This study will use both qualitative and quantitative methods to address important questions. Specifically, semistructured interviews using focus group and interview methodologies will be used to assess youths' lived experiences and will account for specific details that quantitative methods may not be able to attain. In addition, quantitative methods will be used to examine direct and multilevel associations between neighborhood factors and youth substance use and mental health outcomes. RESULTS: A previous analysis from a substance use initiative in Paterson, New Jersey found that youth who use substances such as marijuana and alcohol are more likely to have higher rates of depression and anxiety. On the basis of the research questions, this study will examine the association between neighborhood characteristics, substance use, and mental health symptoms among youth in Paterson by using quantitative and qualitative methods and will use these findings to inform the adaptation of a community- and evidence-based substance use prevention intervention for these youths. CONCLUSIONS: The findings of this study will provide an important contribution to understanding the role of socioecological factors in predicting substance use and mental health outcomes among youth in a lower-resourced, urban community. Furthermore, these findings will serve as evidence for the development of a culturally informed, community-based prevention program to address substance use disparities for youth, including those who are truant in Paterson, New Jersey. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/29427.

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)0.073
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.073
Threshold uncertainty score0.386

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0730.039
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.003
Science and technology studies0.0090.003
Scholarly communication0.0030.003
Open science0.0050.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0480.006

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 head0.847
GPT teacher head0.629
Teacher spread0.218 · 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 designObservational
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

Citations11
Published2021
Admission routes1
Has abstractyes

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