Sexuality Knowledge Assessment Questionnaire (SEKAQ): Importance of the School Setting in Health Education
Bibliographic record
Abstract
ABSTRACT The Sexuality Knowledge Assessment Questionnaire (SEKAQ) was designed to assess sexuality‐related knowledge among adolescents and future health and education professionals, particularly within the context of school‐based health education initiatives. This study developed and validated the SEKAQ, an instrument aimed at evaluating critical aspects of sexuality education, such as sexual risks, STIs, pornography, sexual diversity, gender stereotypes, sexual violence, body image issues, and societal pressures regarding virginity. Developed collaboratively with healthcare professionals and educational institutions, the SEKAQ comprises 11 items, each with multiple‐choice answers and only one correct option. Psychometric evaluation was conducted using data from 454 participants, including high school, nursing, educator, and health sciences students. The SEKAQ demonstrated strong internal consistency, with a Cronbach's ⍺ of 0.84 and an average score of 7.04 (SD = 3.18). Factorial analysis confirmed the reliability and validity of the SEKAQ as a single‐factor tool, identifying knowledge gaps in sexuality to guide healthcare interventions. The results showed higher knowledge among women and nursing students, while younger participants and teachers had lower scores. SEKAQ provides a valuable tool for health and education professionals to evaluate sexuality knowledge, supporting the development of sexual well‐being education strategies for teenagers and youth.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".