Exploring Canadian Children’s Social Media Use, Digital Literacy, and Quality of Life: Pilot Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: Understanding social media use and digital literacy among young Canadian children is an increasing area of concern, given the importance of digital inclusion for full and informed participation in evolving educational, civic, corporate, social, and economic spaces. OBJECTIVE: The aim of this study was to explore internet and social media knowledge as well as social media use among Canadian children aged between 6 and 10 years. METHODS: We conducted interview surveys with 42 children aged between 6 and 10 years who participated in an after-school health promotion program in an urban community in Southwestern Ontario to understand their digital literacy skills and social media use. The data were analyzed using both quantitative and qualitative methods. RESULTS: Of the 42 children who participated in this study, 24 (57%) reported that they used social media, specifically YouTube (19/24, 79% reported use), Snapchat (16/24, 67% reported use), and Facebook (8/24, 33% reported use). While using social media, children reported sharing personal information, including videos or pictures of themselves (12/24, 50%), videos or pictures of others (8/24, 33%), and their birthday (12/24, 50%), whereas only one-third (9/24, 38%) of the children believed that only close family and friends had access to the content they shared. When reporting on the quality of life in the context of using social media, most (17/24, 71%) children never felt sad, half (12/24, 50%) never had difficulty making new friends, and nearly one-third (7/24, 30%) indicated that they never had difficulty wanting to play outside. CONCLUSIONS: Owing to the rapidly evolving uptake and use of social media among young Canadians, the implementation of childhood digital health literacy education is vital to best support digital inclusion and well-being in Canada. The findings of our study highlight the need for future research to understand where children receive their digital literacy knowledge from and whether this knowledge is gained through self-directed social media use or observation from other actors, such as parents, siblings, or friends.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".