Socio-Emotional Impacts of Playing Massively Multiplayer Online Role-Playing Games (MMORPGs) on Older Adults
Bibliographic record
Abstract
The proportion of people aged 60 and over is growing faster than any other age group. Due to shift from career or family focus, loss of long-term companions and increasing likelihood of chronic and debilitating illness, older adults face some key social and psychological challenges such as loneliness, depression and lack of social support. Gerontology researchers have demonstrated that social interaction is an important component of successful aging. Massively Multiplayer Online Role-Playing Games (MMORPGs) can offer older adults many opportunities to maintain current and develop meaningful and supportive relationships. Drawing on the challenges facing older adults and prior theoretical and empirical studies, this research explored the social and emotional impacts of playing MMORPGs on older adults aged 55 and over, primarily analyzing the relationships between older adults’ social interactions in MMORPGs and six social and emotional factors (i.e., bridging and bonding social capital, loneliness, depression, social support and belongingness). To address this question, four research hypotheses were generated. An online survey was developed and published to eight World of Warcraft (WoW, a popular MMORPG) player forums to gather information about older gamers’ demographic characteristics, play patterns, social interactions in WoW, measurements of six social-emotional dimensions, and challenges facing older adults while playing WoW. Data were collected over a three and half months period (from May 15th to September 1st, 2014) from a sample including 222 WoW players aged 55 and older. To answer the research questions and test the four hypotheses, hierarchical multiple regression analysis was applied, and Cohen’s f2 was computed to compare effect sizes. Similar to their younger counterparts, older adults’ social interactions in MMORPGs can take place on several different levels, and can be casual or intimate, and even romantic. Social interaction in MMORPGs is an important source for older adults’ social learning. The regression analyses revealed that enjoyment of relationships and quality of guild play has deep impacts on older adults’ social and emotional wellbeing.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".