Protective Factors in the Lives of Bisexual Adolescents in North America
Bibliographic record
Abstract
OBJECTIVES: We compared protective factors among bisexual adolescents with those of heterosexual, mostly heterosexual, and gay or lesbian adolescents. METHODS: We analyzed 6 school-based surveys in Minnesota and British Columbia. Sexual orientation was measured by gender of sexual partners, attraction, or self-labeling. Protective factors included family connectedness, school connectedness, and religious involvement. General linear models, conducted separately by gender and adjusted for age, tested differences between orientation groups. RESULTS: Bisexual adolescents reported significantly less family and school connectedness than did heterosexual and mostly heterosexual adolescents and higher or similar levels of religious involvement. In surveys that measured orientation by self-labeling or attraction, levels of protective factors were generally higher among bisexual than among gay and lesbian respondents. Adolescents with sexual partners of both genders reported levels of protective factors lower than or similar to those of adolescents with same-gender partners. CONCLUSIONS: Bisexual adolescents had lower levels of most protective factors than did heterosexual adolescents, which may help explain their higher prevalence of risky behavior. Social connectedness should be monitored by including questions about protective factors in youth health surveys.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".