Prevalence and influencing factors of social isolation among community elderly in Tangshan city
Bibliographic record
Abstract
Objective To investigate the status quo and influencing factors of social isolation among community elderly and to provide evidences for improving the quality of life of the elderly. Methods Using convenient sampling, we recruited 1 526 residents aged 60 years and above in 6 communities of Tangshan city, Hebei province for face-to-face interviews conducted from December 2017 to August 2018. A self-designed questionnaire, the Abbreviated Lubben Social Network Scale (LSNS-6), Emotional Balance Scale, Social Support Rating Scale and Montreal Cognitive Assessment Scale were used in the survey. Results For all the participants averagely aged 71.08 ± 7.729 years, the mean score of LSNS-6 was 16.16 ± 5.447 and 371 (24.3%) were assessed as being with social isolation (LSNS-6 score of ≤ 12). Univariate analysis revealed following significant influencing factors of social isolation: demographic characteristics (age, education, marital status, number of children), health condition (chronic illness, physiological perception, cognitive function), psychological status (emotional balance, negative life event, interpersonal well-being), family environment (family structure and grandparenting) and social environment (social support and community function) (P Conclusion Multiple factors have significant impact upon social isolation among elderly community residents in Tangshan city and strengthening familial and social support can reduce the risk of social isolation in the elderly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".