Girl2Girl: How to develop a salient pregnancy prevention program for cisgender sexual minority adolescent girls
Bibliographic record
Abstract
INTRODUCTION: Although sexual minority girls are more likely than heterosexual girls to be pregnant during adolescence, programs tailored to their needs are non-existent. Here we describe the iterative development of Girl2Girl, a text messaging-based pregnancy prevention program for cisgender lesbian, gay, bisexual and other sexual minority (LGB+) girls across the United States. METHODS: Four activities are described: 1) 8 online focus groups to gain feedback about intended program components (n = 160), 2) writing the intervention content, 3) 4 online Content Advisory Teams that reviewed and provided feedback on the salience of drafted intervention content (n = 82), and 4) a beta test to confirm program functionality, the feasibility of assessments, and the enrollment protocol (n = 27). Participants were 14-18-year-old cisgender LGB+ girls recruited nationally on social media. Across study activities, between 52% and 70% of participants were 14-16 years of age, 10-22% were Hispanic ethnicity, and 30-44% were minority race. RESULTS: Focus group participants were positive about receiving text messages about sexual health, although privacy was of concern. Thus, better safeguards were built into the enrollment process. Teens in the Content Advisory Teams found the content to be approachable and compelling, although many wanted more gender-inclusive messaging. Messages were updated to not assume people with penises were boys. Between 71 and 86% of participants in the beta test provided weekly feedback, most of which was positive; no one withdrew during the seven-week study period. CONCLUSIONS: This careful step-by-step iterative approach appears to have resulted in a high level of intervention feasibility and acceptability.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".