One-Year Follow-up After a Pregnancy Prevention Intervention for LGB+ Teens: An RCT
Bibliographic record
Abstract
BACKGROUND: Lesbian, gay, bisexual, and other sexual minority (LGB+) girls are more likely than heterosexual girls to be pregnant during adolescence. Nonetheless, LGB+ inclusive pregnancy prevention programming is lacking. METHODS: Between January 2017 and January 2018, 948, 14 to 18 year-old cisgender LGB+ girls were enrolled in a national randomized controlled trial. Girls were assigned either to Girl2Girl or an attention-matched control group. They were recruited via social media and enrolled over the telephone. The 5-month intervention consisted of a 7-week program (4-12 text messages sent daily) and a 1-week booster delivered 12 weeks later. Longitudinal models of protected sex events had a negative binomial distribution and a log link function. Longitudinal models examining use of birth control assumed a Bernoulli distribution of the outcome variable and a logit link function. Models adjusted for baseline rate of the outcome, age, and a time-varying indicator of sexual experience. RESULTS: Girl2Girl participants had higher rates of protected penile-vaginal sex events over time compared with controls. Girl2Girl participants also were more likely than control participants to report use of birth control other than condoms. Models of abstinence and pregnancy rates did not suggest statistically significant group differences across time. However, effect sizes were in the small to medium range and point estimates favored Girl2Girl versus control in both cases. CONCLUSIONS: Girl2Girl is associated with sustained pregnancy preventive behaviors for LGB+ girls through 12 months postintervention. Text messaging could be considered as a viable method to increase access to sexual health programming to adolescents nationally.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".