Interventions in ambulatory healthcare settings to reduce social isolation among adults aged 18–64: a systematic review
Bibliographic record
Abstract
BACKGROUND: Social isolation is associated with increased all-cause and premature mortality, poor chronic disease management, and mental health concerns. Limited research exists on interventions addressing social isolation among individuals under 65 despite its increasing prevalence among young and middle-aged adults. AIM: To identify interventions from the extant literature that address social isolation and loneliness in ambulatory healthcare settings in adults aged 18-64 and to identify elements of successful studies for future intervention design. DESIGN & SETTING: Systematic review of interventions targeting social isolation in community-dwelling adults aged 18-64 within ambulatory healthcare settings. METHOD: A search strategy was developed to identify relevant articles in the following databases: Ovid MEDLINE, Embase, EBM Reviews, Scopus, CINAHL, and PsychInfo. Data were extracted on study design and setting, intervention type, outcome related to social isolation/loneliness, and scale of measure used. RESULTS: 25 078 citations were identified and underwent title and abstract screening. 75 articles met our inclusion criteria and were synthesised, including an assessment of bias. Effective interventions were delivered in community health settings, incorporated a group component, and used digital technologies. They also addressed the association between mental health and social isolation using cognitive-behavioural therapy (CBT) approaches and enhanced self-management and coping strategies for chronic conditions through psycho-educational interventions. CONCLUSION: Future research should prioritise adults living in low-income and middle-income countries, racialised individuals, as well as those with fewer educational opportunities. There is also a need to advance research in primary care settings, where longitudinal patient-provider relationships would facilitate the success of interventions.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".