“People play it down and tell me it can’t kill people, but I know people are dying each day”. Children’s health literacy relating to a global pandemic (COVID-19); an international cross sectional study
Bibliographic record
Abstract
The aim of this study was to examine aspects of children's health literacy; the information sources they were accessing, their information preferences, their perceived understanding of and their reported information needs in relation to COVID-19. An online survey for children aged 7-12 years of age and parent/caregivers from the UK, Sweden, Brazil, Spain, Canada and Australia was conducted between 6th of April and the 1st of June 2020. The surveys included demographic questions and both closed and open questions focussing on access to and understanding of COVID-19 information. Descriptive statistics and qualitative content analysis procedures were conducted. The findings show that parents are the main source of information for children during the pandemic in most countries (89%, n = 347), except in Sweden where school was the main source of information. However, in many cases parents chose to shield, filter or adapt their child's access to information about COVID-19, especially in relation to the death rates within each country. Despite this, children in this study reported knowing that COVID-19 was deadly and spreads quickly. This paper argues for a community rather than individual approach to addressing children's health literacy needs during a pandemic.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".