An mHealth Intervention for Pregnancy Prevention for LGB Teens: An RCT
Bibliographic record
Abstract
BACKGROUND: Although lesbian, gay, bisexual and other sexual minority (LGB+) girls are more likely than heterosexual girls to be pregnant during adolescence, relevant pregnancy prevention programming is lacking. METHODS: A national randomized controlled trial was conducted with 948 14- to 18-year-old cisgender LGB+ girls assigned to either Girl2Girl or an attention-matched control group. Participants were recruited on social media between January 2017 and January 2018 and enrolled over the telephone. Between 5 and 10 text messages were sent daily for 7 weeks. Both experimental arms ended with a 1-week booster delivered 12 weeks subsequently. RESULTS: A total of 799 (84%) participants completed the intervention end survey. Participants were, on average, 16.1 years of age (SD: 1.2 years). Forty-three percent were minority race; 24% were Hispanic ethnicity. Fifteen percent lived in a rural area and 29% came from a low-income household. Girl2Girl was associated with significantly higher rates of condom-protected sex (adjusted odds ratio [aOR] = 1.48, P < .001), current use of birth control other than condoms (aOR = 1.60, P = .02), and intentions to use birth control among those not currently on birth control (aOR = 1.93, P = .001). Differences in pregnancy were clinically but not statistically significant (aOR = 0.43, P = .23). Abstinence (aOR = 0.82, P = .34), intentions to be abstinent (aOR = 0.95, P = .77), and intentions to use condoms (aOR = 1.09, P = .59) were similar by study arm. CONCLUSIONS: Girl2Girl appears to be associated with increases in pregnancy preventive behaviors for LGB+ girls, at least in the short-term. Comprehensive text messaging–based interventions could be used more widely to promote adolescent sexual health behaviors across the United States.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".