Older Adults’ Digital Gameplay
Bibliographic record
Abstract
Background. Empirical evidence suggests that digital gameplay can enhance social interaction and improve cognition for older adults. However, if digital games are to be effectively used as interventions to address age-related challenges, it is important to explore older adults’ experiences in playing them. Aim. The purpose of this survey design study was to identify digital gameplay patterns, perceived socio-emotional and cognitive benefits, and difficulties encountered in the gameplay experiences of older adults. Method. Adults aged 55 or older, recruited from seniors’ centers and local shopping malls in a Canadian city, responded to a printed, mainly closed-ended questionnaire. Results. 463 respondents reported that they actively play digital games. Most played alone rather than with others, and most rated themselves as intermediate or expert players. Players self-reported cognitive benefits but few socio-emotional benefits and few difficulties. Conclusions. The results of this study show promise for the use of digital games to provide innovative and engaging activities for enhancing older adults’ aging processes. Significant associations were found between player skill level and reported benefits. Recommendations. To perceive these benefits, older adults need to play frequently enough to develop beyond a beginner level. Education, facilitation, and support may be needed to encourage older adults to realize socio-emotional benefits from digital gameplay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".