Determinants of Social Activity Among Geriatric Patients in Northern Romania: A Cross-Sectional Study
Bibliographic record
Abstract
Background/Objectives: The aging population poses a significant challenge to global public health, impacting the physical, mental, and social health of older adults. Social activity has been widely acknowledged as a protective factor for both mental and physical well-being. Research indicates that consistent engagement in social activities can mitigate the risk of depression, prevent cognitive decline, and support physical functionality. This study aims to explore the correlations and associations between two variables related to social activity (self-reported activity level and time spent with friends) and various other variables among geriatric patients in Northern Romania. Methods: This cross-sectional, single-center observational study utilized data from 588 geriatric patients (402 females and 186 males) admitted to the Geriatrics ward of the Municipal Clinical Hospital. The dataset included variables such as sociodemographic information, Geriatric Depression Scale (GDS), Montreal Cognitive Assessment (MoCA), and SARC-F questionnaire scores, time spent with friends, and activity levels. Descriptive statistics were computed alongside statistical tests to examine group differences, associations, and predictive relationships. Results: The sample was characterized by variability in age, educational attainment, and pension levels. The statistical analyses revealed significant differences based on education, pension, and civil status. Patients with higher GDS and SARC-F scores had lower odds of spending time with friends or belonging to the active or extremely active groups. Notably, women reported higher GDS scores and lower activity levels compared to men. Conclusions: Understanding the factors that influence social activity among older adults is essential for designing targeted interventions aimed at preventing social isolation and fostering healthy aging across diverse demographic and environmental contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".