How Sex Education Effects Sexual Practices: The relationship between high school sexual health education and subsequent sexual practices in high school and college
Bibliographic record
Abstract
This study sought to determine the relationship between high school sexual health education programming and subsequent sexual behaviors in high school and college, asking three primary research questions: How does the comprehensiveness of an individual’s sex education program in high school influence their sexual behaviors in high school in terms of frequency, agency, pleasure, and safety? How does the comprehensiveness of an individual’s sex education in high school influence their sexual behaviors in college in terms of frequency, agency, pleasure, and safety? And among those who took sexual health education courses, what is the relationship between curricular characteristics and students’ identities?\nThis study answered these questions through both qualitative and quantitative means with a survey sent to a collegiate undergraduate population asking students to reflect on their sexual health education participation in high school and subsequent sexual practices in high school and college. There were two primary findings of this study: that more abstinence-plus sex education content was positively correlated with higher frequencies of sexual practices in high school and that the socioeconomic status (SES) of an individual impacted access to the sexual health education programs students experienced in terms of duration and content.\nThese findings have implications for how we design and implement sexual health education programming 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".