Measuring the Knowledge and Perception of Riyadh Residents Regarding the Presence of Health Educators in Schools
Bibliographic record
Abstract
BACKGROUND: Health educators play a crucial role in promoting student well-being and fostering healthy behaviors. Despite increasing health concerns among adolescents in Saudi Arabia, such as obesity and mental health disorders, public perceptions of health educators in schools remain underexplored. OBJECTIVES: This study assesses Riyadh residents’ knowledge, perceptions, and support for integrating health educators into educational institutions. MATERIALS & METHODS: A cross-sectional survey was conducted among 418 Riyadh residents aged 18 and older, using a convenience sampling approach. An electronic questionnaire measured demographic characteristics, perceived benefits, and opinions on the presence of health educators. Results Data were analyzed using descriptive statistics, t-tests, ANOVA, and regression analysisin SPSS. RESULTS: A majority (97%) supported the presence of health educators in schools, with health professionals rating their benefits higher (mean = 4.7) than non-health professionals (mean = 4.5, p < 0.001). Despite strong support, 78% of participantsreported never attending a school with a health educator, indicating a gap in educational infrastructure. Regression analysis showed a strong positive association (B = 0.75 ± 0.02, R² = 68.0%, p < 0.001) between perceived benefits and health educator presence, particularly among younger and highly educated participants. CONCLUSION: These findings highlight the need for integrating health educators into Saudi schools, aligning with national health initiatives and Saudi Vision 2030. Policymakers should consider pilot programs and training opportunities to bridge the gap between community expectations and current educational practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".