DO HEALTH-RELATED FACTORS AFFECT SMARTPHONE USE IN ELDERLY INDIVIDUALS
Bibliographic record
Abstract
The purpose of this study was to investigate the impact of physical and cognitive characteristics of elderly individuals living in nursing homes on their smartphone usage. The study included 67 volunteer individuals residing in a nursing home in Muğla. Sociodemographic characteristics and smartphone usage details of the individuals were questioned using a form prepared by the researchers. Participants' physical functions were assessed using the 6-Minute Walk Test, the 5 Times Sit-to-Stand Test, and the Berg Balance Scale. The Montreal Cognitive Assessment Scale (MoCA) was used to examine cognitive function. The mean age of the individuals included in the study was 74.18±3.26 years. A statistically significant moderate positive relationship was observed between the smartphone usage duration and MoCA score of the individuals (r=0.530, p0.05). As per the results of our study, an increase in smartphone usage duration has been seen to be associated with the preservation of cognitive abilities in elderly individuals. We believe that smartphones, which can provide access to various health services when used effectively, as well as meeting the socialization needs of individuals living in nursing homes, play an essential role in promoting active aging.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".