Benefits of Digital Gameplay for Older Adults: Does Game Type Make a Difference?
Bibliographic record
Abstract
Digital games can help older adults to entertain themselves, socialize with others, engage their cognitive functions, and enhance emotional states. This study surveyed 463 older Canadian adults to identify the digital games they had played and investigate whether playing them was associated with perceived socioemotional and cognitive benefits. The most widely reported socioemotional benefits were developing self-confidence, dealing with loneliness, and connecting with family. The most widely reported cognitive benefits were focusing, memory improvement, improved reaction speed, and problem solving. In the socioemotional category, connecting with current friends and connecting with family were both associated with strategy games, while connecting with current friends was also associated with sport games. In the cognitive category, both problem solving and speed in reacting/responding were associated with arcade games. Results show that playing digital games has the potential to be an intervention tool to improve older adults’ wellbeing. Funding: This study was supported by the Social Sciences and Humanities Research Council of Canada (grant number 435-2012-0325) and AGE-WELL NCE Inc., a member of Canada’s Networks of Centres of Excellence (grant number CRP 2015-WP4.2).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".