A Scoping Review of Digital Gaming Research Involving Older Adults Aged 85 and Older
Bibliographic record
Abstract
BACKGROUND: Interest in the use of digital game technologies by older adults is growing across disciplines from health and gerontology to computer science and game studies. The objective of this scoping review was to examine research evidence involving the oldest old (persons 85 years of age or greater) and digital game technology. MATERIALS AND METHODS: PubMed, CINHAL, and Scopus were searched, and 46 articles were included in this review. RESULTS: Results highlighted that 60 percent of articles were published in gerontological journals, whereas only 8.7 percent were published in computer science journals. No studies focused directly on the oldest old population. Few studies included sample sizes greater than 100 participants. Seven primary and 34 secondary themes were identified, of which Hardware Technology and Assessment were the most common. CONCLUSIONS: Existing evidence demonstrates the paucity of studies engaging older adults 85 years of age and above regarding the use of digital gaming and highlights a new understudied cohort for further research focus. Recommendations for future research include intentional recruitment and proportionate representation of participants ≥85 years of age, large sample sizes, and explicit mention of specific numbers of participants ≥85 years of age, which are necessary to advance knowledge in this area. Integrating a rigorous and robust mixed-methods approach including theoretical perspectives would lend itself to further in-depth understanding and knowledge generation in this field.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".