For Whom the Games Toll: A Qualitative and Intergenerational Evaluation of What is Serious in Games for Older Adults
Bibliographic record
Abstract
Abstract The aim of this study was to engage older adults in discussions about digital serious games. Using a qualitative exploratory approach, we report observations from more than 100 h of conversations with individuals in the age range 65–90, in a study entitled “Finding better games for older adults” (June 2017–December 2019). Phase 1 (19 older participants, 3 young research students) involved conversations around a quantitative study of cognitive benefits of digital playing (minimum 6 h/person). Phases 2 and 3 involved a focus group in the form of a community class (10 weeks, 2 h per meeting), involving introduction to digital game genres, playing, and discussing motivations and obstacles for current and future play. Cognitive stimulation, emotional distraction and physical therapy were initially stated as the motives for game play. However, with growing familiarity and voluntary exchanges of personal stories between older and younger participants, the cultural significance of the medium of game (especially with story-telling and VR technology) became more important to older adults. More than mechanical inaccessibility, lack of access to the cultural discourse about games presents barriers for older adults. To create a safe, comfortable and accessible space for intergenerational learning and play is of primary importance both for users and designers, should serious games be considered for the future of digital care.
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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 | 0.031 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".