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Record W4405536722 · doi:10.2196/63114

Adolescent Youth Survey on HIV Prevention and Sexual Health Education in Alabama: Protocol for a Web-Based Survey With Fraud Protection Study

2024· article· en· W4405536722 on OpenAlexvenueno aff
Henna Budhwani, İbrahim Yiğit, Josh Bruce, Christyenne L Bond, Andrea Johnson

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent Sexual and Reproductive Health
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintHuman immunodeficiency virus (HIV)Protocol (science)Reproductive healthAdolescent healthPeer educationPsychologySexual behaviorMedicineHealth educationEnvironmental healthFamily medicinePublic healthPopulationComputer scienceSocial psychologyAlternative medicineNursingWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: In Alabama, the undiagnosed HIV rate is over 20%; youth and young adults, particularly those who identify as sexual and gender minority individuals, are at elevated risk for HIV acquisition and are the only demographic group in the United States with rising rates of new infections. Adolescence is a period marked by exploration, risk taking, and learning, making comprehensive sexual health education a high-priority prevention strategy for HIV and sexually transmitted infections. However, in Alabama, school-based sexual health and HIV prevention education is strictly regulated and does not address the unique needs of sexual and gender minority teenagers. OBJECTIVE: To understand knowledge gaps related to sexual health, HIV prevention, and pre-exposure prophylaxis (PrEP), we conducted the Alabama Youth Survey with individuals aged 14-17 years. In the survey, we also evaluated young sexual and gender minority individuals' preferences related to prevention modalities and trusted sources of health information. METHODS: Between September 2023 and March 2024, we conducted a web-based survey with 14- to 17-year-olds who are assigned male at birth, are sexually attracted to male youth, and lived in Alabama. Half of the study's participants were recruited through community partners, the Magic City Acceptance Academy and Magic City Acceptance Center. The other half were recruited on the web via social media. A 7-step fraud and bot detection protocol was implemented and applied to web-based recruitment to reduce the likelihood of collecting false information. Once data are ready, we will compute frequencies for each measure and construct summary scores of scales, such as HIV and PrEP knowledge, to determine internal consistency. Using multivariable logistic regression, we will examine associations between personal characteristics of survey respondents and key constructs using SPSS 29 (IBM Corp) or SAS 9.4 (SAS Institute). RESULTS: Analyses are ongoing (N=206) and will conclude in June 2025. Preliminary results include a sample mean age of 16.21 (SD 0.88) years; about a quarter identified as transgender or gender nonconforming, with 6% stating their gender as a transgender woman. A total of 30% self-reported their race as African American or Black; 12% were Hispanic or Latinx. More than half reported being sexually active in the past 6 months. Primary data analyses will be completed in mid-2025. If findings are promising, results will be used as preliminary data to support the development of an intervention to address knowledge gaps and prevention preferences. CONCLUSIONS: If the study is successful, it will yield information on HIV knowledge, PrEP awareness, PrEP preferences, and related outcomes among sexual and gender minority teenagers in Alabama, an underserved, hard-to-reach, but also high-priority population for public health efforts to Ending the HIV Epidemic. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63114.

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)0.021
metaresearch head score (Gemma)0.012
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 score0.024
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.012
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0240.008

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 head0.627
GPT teacher head0.662
Teacher spread0.035 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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