Assessing sexual health literacy: a systematic review of measures
Bibliographic record
Abstract
Sexual health literacy refers to the ability to find, understand, and use information and services to inform decisions and actions related to sexual health. Given the importance of sexual health literacy for improving health outcomes, it is prudent to identify sexual health literacy measures that can be used by healthcare providers, scholars and educators. To address this need, we used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to conduct a systematic review. This review examined 12 databases to identify existing sexual health literacy self-report scales, subscales or individual items that were available in English, developed for scale validation purposes, and published between 2002, the year the World Health Organization convened experts to provide a clear working definition of sexual health, and 2022. We conducted a risk of bias and quality assurance assessment of the nine articles that met inclusion criteria, and coded articles along the ten components of a sexual health model used as the theoretical framework. Findings revealed mixed quality of identified measures. None of the measures received positive ratings on all eight criteria assessed or addressed all components of the sexual health model. The results from this systematic review suggest the need for a culturally sensitive, valid and reliable scale to assess sexual health literacy that can be used by sexual health professionals to promote sexual health and to reduce deleterious sexual health outcomes.
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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 | 0.021 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".