Design Preferences for a Serious Game–Based Cognitive Assessment of Older Adults in Prison: Thematic Analysis
Bibliographic record
Abstract
BACKGROUND: Serious games have the potential to transform the field of cognitive assessment. The use of serious game-based cognitive assessments in prison environments is particularly exciting. This is because interventions are urgently needed to address the rapid increase in the number of currently incarcerated older adults globally and because of the heightened risks of dementia and cognitive decline present in this population. Game-based assessments are assumed to be fun, engaging, and suitable alternatives to traditional cognitive testing, but these assumptions remain mostly untested in older adults. This is especially true for older adults in prison, whose preferences and needs are seldom heard and may deviate from those previously captured in studies on cognition and serious games. OBJECTIVE: This study aimed to understand the design preferences of older adults in prison for a game-based cognitive assessment. METHODS: This study used reflexive thematic analysis, underpinned by critical realism, and applied the technique of abduction. Overall, 4 focus groups with a total of 20 participants were conducted with older adults (aged ≥50 years; aged ≥45 years for Aboriginal and Torres Strait Islander people) across 3 distinct prison environments in Australia. RESULTS: Self-determination theory was used as a theoretical foundation to interpret the results. Overall, 3 themes were generated: Goldilocks-getting gameplay difficulty just right through optimal challenge (the first theme emphasizes the participants' collective desire for an individualized optimal level of difficulty in serious gameplay), Avoiding Childish Graphics-gimmicky gameplay can be condescending (the second theme raises the importance of avoiding immature and childlike gameplay features, as some older end users in prison felt that these can be condescending), and A Balanced Diet-meaningful choice and variety keeps game-based assessments fun (the third theme highlights the strong user preference for meaningful choice and variety in any serious game-based cognitive assessment to maximize in-game autonomy). CONCLUSIONS: The collection of these themes provides novel insights into key game design preferences of marginalized older adults.
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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 | 0.033 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".