Testing a school-based program to promote digital health literacy and healthy lifestyle behaviours in intermediate elementary students: The Learning for Life program
Bibliographic record
Abstract
Promoting digital health literacy and healthy lifestyle behaviours in children can lead to positive long-term health outcomes and prevent chronic diseases. However, there are few school-based interventions promoting this education to intermediate elementary students. The objective of this study was to test the effectiveness of a novel intervention to increase students' digital health literacy and health knowledge. Learning for Life is a classroom-based education program, developed for grade 4-7 students and delivered by teachers over six weeks. Three Canadian schools were recruited to deliver the intervention in 2018. This study had a pre-post design and no control group. Students' self-reported digital health literacy and healthy lifestyle behaviours were measured at pre-intervention (n = 126), post-intervention (n = 119), and two-month follow-up (n = 104). Students at pre-intervention had a mean (SD) age of 10.98 (0.56) years (57.1% females). Almost all (97%) students had unsupervised access to the Internet through a computer or smartphone. From pre- to post-intervention, students' digital health literacy increased (p = 0.009), but decreased from post-intervention to follow-up (p < 0.001). Post-intervention, the majority of students could identify at least one healthy behaviour (e.g., exercising one hour/day) and reported making at least one healthy change in their lives (e.g., eating more fruits/vegetables). This study demonstrated that the Learning for Life intervention can improve intermediate elementary students' digital health literacy over the short-term and help them learn and retain healthy lifestyle knowledge and behaviours. These findings affirm the need for interventions promoting digital healthy literacy and healthy lifestyle behaviours for this age group.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".