Determinants of recreational screen time behavior following the COVID-19 pandemic among Canadian adults
Bibliographic record
Abstract
The objectives of our study were to examine recreational screen time behavior before and 2 years following the COVID-19 pandemic lockdown, and explore whether components of the capability–opportunity–motivation–behavior (COM-B) model would predict changes in this recreational screen time behavior profile over the 2-year period. This cross-sectional, retrospective study was conducted in March 2022. Canadian adults ( n = 977) completed an online survey that collected demographic information, current screen time behavior, screen time behavior prior to the pandemic, and beliefs about capability, opportunities, and motivation for limiting screen time based on the COM-B model. We found that post-pandemic recreational screen time (3.91 ± 2.85 h/day) was significantly higher than pre-pandemic levels (3.47 ± 2.50 h/day, p < 0.01). Three recreational screen time behavior profiles were identified based on the Canadian 24-Hour Movement Guidelines: (1) always met screen time guidelines (≤3 h/day) (47.8%; n = 454); (2) increased screen time (10.1%; n = 96); and (3) never met screen time guidelines (42%; n = 399). The overall discriminant function was found to be significant among the groups (Wilks’ λ = 0.90; canonical r = 0.31, χ2 = (14) = 95.81, p < 0.001). The group that always met screen time guidelines had the highest levels of automatic motivation, reflective motivation, social opportunity, and psychological capabilities to limit screen time compared to other screen time profile groups. In conclusion, recreational screen time remains elevated post-pandemic. Addressing motivation (automatic and reflective), psychological capabilities, and social opportunities may be critical for future interventions aiming to limit recreational screen time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".