Healthy sleep for healthy schools: A pilot study of a sleep education resource to improve adolescent sleep
Bibliographic record
Abstract
ISSUE ADDRESSED: Insufficient sleep and unhealthy sleep practices in adolescents are associated with significant health risks. Sleep education programs in schools aim to improve sleep behaviour. A new eLearning sleep education program, Healthy Sleep for Healthy Schools (HS4HS), was developed focused on these goals and is distinguishable from other sleep education programs because it is delivered by teachers, making it more sustainable and adaptable for schools. We aimed to evaluate if HS4HS would improve student sleep knowledge, healthy sleep practices, sleep duration and reduce sleepiness. We also aimed to understand if this intervention could be successfully implemented by trained teachers. METHODS: Teachers trained in sleep delivered HS4HS to 64 South Australian students in year 9 (aged 13-14 years) over 6 weeks during regular school curriculum. A sleep education survey assessing sleep patterns (such as healthy sleep practices, time in bed and sleepiness), and a sleep knowledge questionnaire was completed pre- and post-HS4HS delivery. Evaluations were also completed by teachers. RESULTS: Sleep knowledge and healthy sleep practices significantly improved post intervention. Time in bed on both school days and weekends increased slightly and sleepiness decreased slightly, but these changes were not statistically significant. Teachers found the program useful, comprehensive and easy to incorporate into their curricula. CONCLUSIONS: After short training, teachers can deliver sleep education during class and improve sleep practices in their students. This suggests that this program may offer potential as an effective and useful resource for teachers wanting to include sleep health in their curriculum. SO WHAT?: Sleep is the foundation of good health and teachers can promote and integrate sleep education into their curricula for the first time with this online teacher focussed program, which has the potential to be a sustainable sleep health promotion resource.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".