Seeing Is Believing? A Mixed-Methods Study Exploring the Quality and Perceived Trustworthiness of Online Information About Chronic Conditions Aimed at Children and Young People
Bibliographic record
Abstract
The numbers of children and young people with chronic conditions are increasing. While their information needs may vary, providing health care information can have considerable benefits, including better emotional health, less distress during treatments, and greater satisfaction with medical care. The Internet is increasingly being used to communicate health-related information to children about a range of chronic conditions. However, the quality of such websites is underexplored. Thus, the objectives of this study were to evaluate the reliability and quality of online information for children about chronic conditions using a standardized evaluation tool, and to explore children's and young people's perceptions of quality and trustworthiness regarding online health information. The study consisted of two phases. In Phase 1, websites about common pediatric chronic conditions aimed at children and containing treatment or management options were identified and the quality assessment tool DISCERN was completed. Test-retest and interrater reliability were calculated. In Phase 2, two focus groups with laptop computers were conducted with children and young people with a chronic condition to explore their perceptions of trustworthiness of online health information. In Phase 1, 165 websites were identified and 100 met the criteria and were assessed. The mean DISCERN score of all sites was 48.16 out of 75 (SD = 7.97, range 28-71, min 15 to max 75). Quality scores varied widely across the sample. The internal consistency and interrater reliability scores were both lower than previously reported in studies using the DISCERN to assess information for adults. In Phase 2, two focus groups with a total of six participants aged 11-23 years revealed a relative lack of concern about the quality and trustworthiness of online health information. Older participants reported judging the source and authorship of websites, but other participants did not question the source of the information they found online. Although personal websites were perceived to be less reliable than those from well-known medical institutions, they were still valued by many of the participants. Overall, there were relatively few websites about pediatric chronic conditions aimed at children, with variable quality and reliability. However, DISCERN's use with pediatric websites across a broad spectrum of developmental stages is limited. Children and young people demonstrated a need for more guidance around assessing trustworthiness of online information. A more appropriate quality assessment tool is warranted, which could usefully be employed by health care professionals, children, and parents.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".