Multiple long-term conditions, loneliness and social isolation: A scoping review of recent quantitative studies
Bibliographic record
Abstract
BACKGROUND: Multiple long-term conditions (MLTC), loneliness and social isolation are common in older adults. Recent studies have explored the association of MLTC with loneliness and social isolation. This scoping review aimed to map this current evidence and identify gaps in the literature. METHODS: A scoping review was conducted following the PRISMA guidelines for scoping reviews. Ovid Medline, Embase, CINAHL, The Cochrane Library, PsycInfo, and Bielefeld Academic Search Engine were searched for studies published between January 2020-April 2023. Quantitative studies, published in any language, that assessed the association of MLTC with loneliness and/or social isolation were included. RESULTS: 1827 records were identified and screened. Of these, 17 met inclusion criteria. Most studies were cross-sectional and based on older adults. Studies were conducted in Europe, the US, Canada, and low- and middle-income countries. Ten studies focused on the association between MLTC and loneliness, six assessed the association between MLTC and social isolation and one examined associations with both loneliness and social isolation. Most studies reported a significant cross-sectional association of MLTC with loneliness, but there was weaker evidence for a longitudinal association between MLTC and loneliness and an association between MLTC and social isolation. Studies were heterogenous in terms of measures and definitions of loneliness/social isolation and MLTC, confounders adjusted for, and analytical models used, making comparisons difficult. CONCLUSIONS: Further population-based longitudinal studies using consistent measures and methodological approaches are needed to improve understanding of the association of MLTC with both loneliness and social isolation.
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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 | 0.035 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.033 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".