Three Models to Motivational Mechanism Creation for Enhancing the Elderly’s Adherence to Home-Based Recreational Video Games
Bibliographic record
Abstract
The elderly's inactivity and sedentary lifestyle are the common issues affecting the physical and mental health of the ageing population worldwide. This situation tends to be deteriorating during the quarantine caused by Covid-19 in many countries. Recreational video games, such as exergames and esports, could potentially motivate people of all age groups to stay physically and mentally active through the gamification elements; however, the discussions about the practical motive elements apply to specific elderly groups are still lacking. Meanwhile, recreational video games might require constant innovations to maintain the players' adherence and engagement; however, the existing articles rarely propose systematic approaches/models for extracting the opportunities to optimise the existing motivational mechanisms, not to mention creating new mechanisms. This paper proposes three models that help innovators select the effective motivational mechanism of recreational video games for motivating the elderly above 65 to stay active at home, extract the mechanisms' innovation opportunities, and perform the motivational mechanisms innovation. The paper starts with an extensive literature review to collect the existing motivational mechanisms. Then, the first model is developed for selecting the elderly's most effective motivational mechanisms based on several motivational conditions. The following section generates the second model for extracting the innovation opportunities based on the three characteristics of creativity: novelty, surprisingness, and usefulness. Finally, the third model for optimising and creating the new features of the selected motivational mechanisms is formulated based on the principles of combinational creativity method.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".