Using Digital Platforms in Schools for Prevention and Health Promotion: A Scoping Review
Bibliographic record
Abstract
Objective: Digital platforms for prevention and health promotion (PHP) are now, more than ever, available for use by school professionals, including teachers. However, little is known about what motivates them to use such platforms. A scoping review (ScR) was conducted to identify conditions that promote use by school professionals, including teachers, of PHP digital platforms at schools. Methods: For our ScR, we accessed ERIC, Sociological Abstracts, MEDLINE, PubMed, and Web of Science databases (period 2000-2018) in 3 sectors: education, health, online technologies. For each study, we prepared and validated a summary sheet. Contents dealing with conditions for use were subjected to open coding, grouped into categories, and synthesized. Results: Of the 3639 articles captured, 17 studies were selected. Five conditions emerged: (1) ensuring that the digital platform becomes a reference for PHP activity in schools; (2) that the resources needed for its uptake are mobilized; (3) that it is user-friendly; (4) that the digital platform engages the participation of everyone involved; and (5) that it is linked to existing programs in the school. Conclusions: These results can guide the activities deployed in schools for optimal implementation of PHP programs from digital platforms.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".