Digital Social Media Development for Learning to Promote the Power of Mental Health of the Elderly
Bibliographic record
Abstract
The study aimed to 1) Study the accessibility and use of digital technology in the elderly; 2) Examine the peculiarities of elderly people’s use of social media and the differences between elderly persons who use and do not utilize social media; and 3) Create the social media that affect the elderly in terms of loneliness reduction. The study sample was 50 people aged over 60 years, derived by purposive selection criterion. The experimental plan One-shot case design. Data was analyzed using mean, standard deviation (SD), percentage, correlations, and t-test. Study findings shown that 1) The majority of the elderly had communication devices. Two elderly people do not have a communication device, but forthyeight others have and use it for different reasons. As a result, using a communication device to address the problem of loneliness among the elderly is possible. 2) The findings with Less Lonely application cater to the needs of the elderly. The specialist indicated that the Less Lonely digital application was of Suitability level “the most” (X = 4.62, SD = 0.40) and efficiency trials passed the 80/80 criteria. The percentage result of the One-to-One testing (80.67%), the small group efficacy findings were efficient (E1/E2) 80.18/82.00, the field group efficacy findings were efficient (E1/E2) 80.16/80.90. 3). The correlation between the amount of time spent on social networking apps and feelings of loneliness showed a significant correlation which a negative relationship. The social media affected the elderly in terms of loneliness reduction is Less Lonely application allow elderly people to engage with other friends. The results were found between the time spent on social media devices and their state of loneliness was lower.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".