Age and Gender Perspectives on Social Media and Technology Practices during the COVID-19 Pandemic
Bibliographic record
Abstract
Few studies have examined social media and technology use during the COVID-19 pandemic in Canada. Therefore, the main research question and objective of this study was to examine similarities and differences in the influences of mobile technology and social media use on Canadians among different age groups and across gender during the COVID-19 pandemic. From June through October 2021, 204 persons completed a 72-item online survey. Survey questions encompassed COVID-19 pandemic experiences and technology use. Standardized measures including the Psychological Wellbeing measure, eHeals, and the UCLA V3 Loneliness scale were collected to examine the psychological influences of the COVID-19 pandemic. Findings showed that males under 50 years were most likely to self-isolate compared to the other demographic results of the study. Males reported using technology less than females but were more likely to report using technology to share information regarding COVID-19. Respondents under 50 years were also more likely to use smartphones/mobile phones as their most used mobile technology device, whereas respondents over 50 were more split between smartphones/mobile phones and computers/tablets as their most used device. Males scored higher on the UCLA loneliness scale and lower on the Psychological Wellbeing sub-scores compared to females. Further research should explore additional demographics in relation to broader aspects of digital skills across different age groups.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".