Recreational Screen Time Use among a Small Sample of Canadians during the First Six Months of the COVID-19 Pandemic
Bibliographic record
Abstract
(1) Background: The coronavirus (COVID-19) pandemic has caused disruptions in the daily lives of individuals in Canada. Purpose: Examine how total and specific (i.e., watching television, using social media, going on the Internet, playing video games, and engaging in virtual social connection) recreational screen time behaviours changed throughout the first six months of the COVID-19 pandemic, in comparison to pre-pandemic levels; (2) Methods: Sixty four Canadians (mostly Caucasian, female, age range = 21-77 years) completed monthly surveys from April to September of 2020; (3) Results: A one-way repeated measures analysis of variance (RM-ANOVA) and subsequent post hoc analysis revealed that total recreational screen time was statistically higher in late March/April (292.5 min/day ± 143.0) and into May, compared to pre-COVID-19 (187.8 min/day ± 118.3), before declining in subsequent months; (4) Conclusions: Generally, specific recreational screen time behaviours, such as time spent watching television, followed the same trend. Future studies with larger sample sizes and from other countries examining recreational screen time behaviours longitudinally over the pandemic are still needed to allow for greater generalizability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".