Acceptability and Feasibility of Peer-to-Peer Text Messaging Among Adolescents to Increase Clinic Visits and Sexually Transmitted Infection Testing: Interrupted Times-Series Analysis
Bibliographic record
Abstract
BACKGROUND: Adolescents are disproportionately affected by sexually transmitted infections (STIs), including HIV. Many youths with asymptomatic STI or related symptoms do not seek treatment and may not be screened if accessing the health care system for other reasons. OBJECTIVE: We examined intervention completion and changes in the number of new patients, the number of STI or HIV tests, and the sexual risk profile of patients over time to determine the feasibility and acceptability of a peer-driven text messaging strategy to connect youth to STI and HIV services. METHODS: The intervention enlisted consecutive patients at an adolescent medicine clinic to send a text message to 5 peers they believed were sexually active and lived in the clinic's service area. The intervention was evaluated using an interrupted time-series design in which baseline clinic service levels were documented during a 35-week lead-in period, followed by a 20-week intervention implementation period, and a 16-week period of continued clinic observation. Clinic and patient data were obtained through chart abstraction from intake forms that occurred during the entire study period. Analyses conducted in 2015 used a generalized linear mixed model. RESULTS: Of the 153 patients approached to participate, 100 agreed to send SMS text messages. Most (n=55, 55%) reported no concerns with sending the text message. No adverse events or negative outcomes were reported. Adolescent STI testing, positive test results, and reported risk behavior increased post intervention, although this was not statistically significant, likely because of the small sample size. CONCLUSIONS: Given low youth uptake of health care services, and STI/HIV screening, in particular, new strategies are needed to address access barriers. Common approaches for reaching youth are resource-intensive and often miss those not connected to school or community programs. The peer-based text messaging strategy showed promise for both increasing the number of youths accessing health services and finding youths engaging in sexual risk behaviors and most in need of sexual health screening and services.
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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 | 0.026 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".