Sociodemographic differences in recreational screen time before and during the COVID-19 pandemic in Canada
Bibliographic record
Abstract
Background: Over the last several years, recreational screen time has been increasing. During the COVID-19 pandemic, recreational screen time rose among Canadian youth and adults, and those who increased screen time had poorer self-reported mental health compared with those who decreased or maintained their recreational screen time levels. Data and methods: Using data from the 2017, 2018, and 2021 Canadian Community Health Survey, the prevalence of meeting the recreational screen time recommendation from the Canadian 24-Hour Movement Guidelines was compared before and during the pandemic across sociodemographic groups. Logistic regression was used to identify sociodemographic groups that were more likely to meet the recreational screen time recommendation before and during the pandemic. Results: The amount of time Canadians spent engaging in daily recreational screen time increased from 2018 to 2021, leading to fewer youth and adults meeting the recreational screen time recommendation during the pandemic compared with before. The prevalence of meeting the recommendation was lower during the pandemic compared with before the pandemic among almost all sociodemographic groups. Among youth, living in a rural area was associated with a greater likelihood of meeting the recommendation before and during the pandemic. Among adults, the following characteristics were all associated with a greater likelihood of meeting the recommendation during the pandemic: being female; living in a rural area or a small population centre; identifying as South Asian; being an immigrant to Canada; living in a two-parent household; being married or in a common-law relationship or widowed, separated, or divorced; working full time; and being a health care worker. Interpretation: The prevalence of meeting the recreational screen time recommendation during the pandemic was lower overall compared with before the pandemic. Several sociodemographic groups were more likely to meet the recommendation during the pandemic. Continued surveillance of recreational screen time is necessary to monitor the indirect effects of the pandemic and to identify population subgroups that would benefit from tailored interventions in the pandemic recovery period.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".