Promising Approaches for Engaging Youth and Young Adults Living with HIV in HIV Primary Care Using Social Media and Mobile Technology Interventions: Protocol for the SPNS Social Media Initiative
Bibliographic record
Abstract
BACKGROUND: In the United States, disparities in the rates of HIV care among youth and young adults result from the intersections of factors that include stigma, substance use, homelessness or marginal housing, institutional neglect, and mental health issues. Novel interventions are needed that are geared to youth and young adults. OBJECTIVE: In this paper, we aim to describe the interventions used by participating sites for Using Social Media initiative, the process for classifying the intervention components, and the methods for conducting a comprehensive evaluation of the interventions. METHODS: In 2015, the Health Resources and Services Administration (HRSA) HIV/AIDS Bureau, Special Projects of National Significance (SPNS) funded the Evaluation and Technical Assistance Center (ETAC) at the University of California, Los Angeles and 10 demonstration projects at sites across the United States that incorporated innovative approaches using a variety of social media and mobile technology strategies designed specifically for youth and young adults living with HIV. The ETAC developed a typology, or a classification system, that systematically summarizes the principal components of the interventions into broader groups and developed a multisite, mixed-methods approach to evaluate them based on the Department of Health and Human Services HIV health outcomes along the HIV care continuum. The mixed-methods approach is key to remove potential biases in assessing the effectiveness of demonstration projects. RESULTS: This SPNS project was funded in September 2015, and enrollment was completed on May 31, 2018. A total of 984 participants have been enrolled in the multisite evaluation. Data collection will continue until August 2019. However, data analysis is currently underway, and the first results are expected to be submitted for publication in 2019. CONCLUSIONS: This HRSA-funded initiative seeks to increase engagement in HIV medical care, improve health outcomes for people living with HIV, and reduce HIV-related health disparities and health inequities that affect HIV-positive youth and young adults. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/10681.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".