Enhancing digital citizenship of children and youth with Autism: Evaluating novel screen time guidelines for caregivers and professionals
Bibliographic record
Abstract
• Supporting the digital citizenship of autistic children is a concern. • We assessed the usability of a recently developed evidence-based website. • The website suggests guidelines and strategies to support healthy screen time use. • We evaluated the overall usability of the screen time guidelines website. • The website is beneficial to support the digital citizenship of autistic children Supporting the development of digital citizenship in children and youth with autism is a concern for many families and clinicians. Nevertheless, there is a substantial gap between the practical applications of research evidence, dissemination methods and available services. In the current study, we assessed the usability of a recently developed evidence-based website, which encompasses guidelines and strategies intended to aid caregivers and professionals in fostering digital citizenship among autistic children and youth. The study included 60 professionals actively working with autistic children and youth, 15 caregivers of autistic children and youth, and 13 participants identifying as both professionals and caregivers. Participants interacted with the website, and then their perspectives on the novel guidelines were gathered through a survey. This survey evaluated vital aspects such as accessibility, organization, clarity, relevance, delivery method, and overall usability of the screen time guidelines website. The results suggest that the newly developed guidelines for children and youth with autism are perceived as clear, well-organized, accessible, and relevant by the participants. Additionally, participants provided valuable insights to enhance the website. They agreed that an online platform is a practical and feasible means to disseminate these novel guidelines. The novel guidelines were found to be a beneficial tool for both professionals and families to support the digital citizenship of children and youth with autism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".