Digital HIV Care Navigation for Young People Living With HIV in San Francisco, California: Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: HIV continues to be a public health challenge adversely affecting youth and young adults, as they are the fastest-growing group of new HIV infections in the United States and the group with the poorest health outcomes among those living with HIV. HIV prevention science has turned to mobile health as a novel approach to reach and engage young people living with HIV (YPLWH) experiencing barriers to HIV care. OBJECTIVE: This study aimed to assess the feasibility and acceptability of a text message-based HIV care navigation intervention for YPLWH in San Francisco. Health eNavigation is a 6-month text message-based HIV care navigation where YPLWH are connected to their own HIV care navigator through text messaging to improve engagement in HIV primary care. Digital HIV care navigation included delivery of the following through SMS text messaging: (1) HIV care navigation, (2) health promotion and education, (3) motivational interviewing, and (4) social support. METHODS: We evaluated the feasibility and acceptability of a text message-based HIV care navigation intervention among YPLWH. We assessed feasibility using quantitative data for the overall sample (N=120) to describe participant text messaging activity during the intervention. Acceptability was assessed through semistructured, in-depth interviews with a subsample of 16 participants 12 months after enrollment. Interviews were audio-recorded, transcribed, and analyzed using grounded theory. RESULTS: Overall, the text message-based HIV care navigation intervention was feasible and acceptable. The majority of participants exhibited medium or high levels of engagement (50/120 [41.7%] and 26/120 [21.7%], respectively). Of the majority of participants who were newly diagnosed with HIV, 63% (24/38) had medium to high engagement. Similarly, among those who were not newly diagnosed, 63% (52/82) had medium to high engagement. The majority of participants found that the intervention added value to their lives and improved their engagement in HIV care, medication adherence, and viral suppression. CONCLUSIONS: Text message-based HIV care navigation is a potentially powerful tool that may help bridge the gaps for linkage and retention and improve overall engagement in HIV care for many YPLWH. Our results indicate that participation in text message-based HIV care navigation is both feasible and acceptable across pervasive structural barriers that would otherwise hinder intervention engagement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".