Measuring social isolation in older adults: A rapid review informing evidence-based research and practice
Bibliographic record
Abstract
OBJECTIVES: Older adults account for 18.5% of the Canadian population and are at risk of experiencing social isolation, compared to other age groups. Researchers define social isolation as a lack of social contact and relationships, but many social isolation measures do not reflect this definition. The aim of our study is to review the existing measures of social isolation with older adults to recommend evidence-based measures to researchers and practitioners. METHODS: We conducted a rapid review on PsycInfo and PsycTests. We included articles that were written in English or French, were peer-reviewed, used an older adult sample, included a self-report social isolation measure, and reported psychometric information. RESULTS: Following exclusion of ineligible articles, 12 measures were available for analysis. We further categorized the measures into: five most recommended measures, five measures that require further research, and two measures not recommended for use with older adults. CONCLUSIONS: We observed a range of measures with varying suitability to be used with older adults; some were empirically driven but did not have strong psychometric properties, or vice-versa. CLINICAL IMPLICATIONS: It is imperative that interventions aimed to address social isolation in older adults use evidence-based measures to assess progress and report treatment effectiveness.
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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 | 0.025 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".