Focusing on Emotional and Social Intelligence Stimulation of People With Dementia by Playing a Serious Game—Proof of Concept Study
Bibliographic record
Abstract
Context: Dementia is one of the top five chronic diseases, which has an overwhelming impact on patients' life quality, family, and caregivers. Currently, research relating to people with dementia (PwD) focuses on the deterioration of cognitive abilities. A more innovative approach, and the one taken by this paper, is focusing on methods to maintain and improve functionality, communication and quality of life of PwD by building on remaining capacities in the yet unexplored domain of emotional and social intelligence (ESI). The use of serious games for PwD (SG4D) aimed at building social and emotional capacity is a budding field of research. Objectives: Proof of concept that the, low cost, easy to deploy SG4D, called “My Brain Works” (MBW), co-designed with PwD, enhances ESI, based on the Bar-On ESI model. Methods: 27 PwD, clients at MELABEV dementia day center, participated in a mixed methods 12 weeks pilot, proof of concept study using a tablet SG4D co-designed with PwD. Quantitative performance data was collected automatically by the tablet during game sessions. In this paper we focus on the analysis of the qualitative and quantitative data related to ESI, observed by 10 different researchers, during each game session. Results: Quantitative data revealed: both the PwD with high and low MoCA scores had similar average ESI scores. Qualitative analysis revealed that the PwD demonstrated 9 sub-components of the Bar-On ESI Model. Conclusion: While there is no drug to stop cognitive decline associated with dementia, interventions related to ESI, on the other hand, may improve functioning and quality of life. Despite declines in cognitive abilities, our study shows that a tablet based SG4D can stimulate their ESI and evoke responses in self-awareness, empathy, social and communication capacities. Using SG4D to exercise and maintain social skills is an area that may be promising in the future and may help counter the negative effects of social isolation and loneliness. Such games, while not focusing on cognitive improvement, may also impact on cognitive functioning and help bridge the gap between caregiver and PwD. More research is needed with larger sample sizes.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".