Latent profile analysis of loneliness among elderly people in the community and its relationship with cognitive function
Bibliographic record
Abstract
Objectives To explore the latent profiles of loneliness in community-dwelling older adults and to explore the relationship between categories and cognitive functioning to inform targeted interventions. Methods A survey was conducted on 658 elderly individuals from 9 communities in Henan Province using the Simplified Loneliness Scale and the Montreal Cognitive Assessment Scale. Mplus8.3 was used for latent profile analysis, and SPSS26.0 software was used to compare the cognitive function differences of elderly people with different types of loneliness. Results Prevalence rate of mild cognitive impairment in community-dwelling older adults 31.00% (204/658). The latent profiles of loneliness symptoms in community-dwelling older adults can be categorized into three latent profiles: low loneliness group (54.4%), social loneliness group (24.3%), and emotional loneliness group (21.3%). Community-dwelling older adults in the emotional loneliness group had a higher risk of cognitive impairment compared to the low loneliness group (OR = 1.693, p < 0.05). Conclusion Three categories of loneliness exist in community-dwelling older adults, with differences in cognitive functioning among community-dwelling older adults with different latent profiles. Community healthcare workers should pay attention to the loneliness of older adults during cognitive function screening, and especially provide psychological counseling to emotionally isolated community-dwelling older adults in order to reduce the risk of cognitive impairment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".