Digital tools to promote or measure health literacy in children aged 3–5 years: scoping review
Bibliographic record
Abstract
Health literacy is a determinant of health that should be promoted and measured in early childhood. In the era of digitalization, this could involve digital platforms. However, little knowledge exists on the extent, range, and nature of literature on digital tools for promoting or measuring young children's health literacy. Addressing this gap, a scoping review was undertaken to explore what digital health literacy tools for children aged 3-5 years could be identified in the literature, what their key features were, how they were designed, whether children were codesigners, and whether the tools had been evaluated. Eligibility criteria included the literature being peer-reviewed, published between 2013 and 2024, and in English. Nine health and education databases were searched, and 19 articles met the inclusion criteria. Few of the reported tools covered the core dimensions of health literacy, underlying the need for digital tools that promote and/or measure young children's health-related knowledge and information-related skills. There was sparse description of design approaches, and little evidence children were engaged as active design partners, which is critical to address. Encouragingly, some evidence was usually provided to rationalize choices around specific digital technologies and/or design features, which could be further bolstered with evidence from the field of educational technology for children. There is strength in the literature's reporting of evaluation studies using well-respected design approaches; however, sample sizes were sometimes small, long-term follow-up did not often occur, and the influence of contextual factors on children's use of the tools was not explored.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".