A systematic review of measures of social connection for people living in long‐term care homes
Bibliographic record
Abstract
Abstract Background Social connection is important for quality of life and care in long‐term care (LTC) homes. However, measuring social connection in LTC residents is challenging due to the high prevalence of dementia and the distinct care environment and activities, so research is limited by a lack of consensus on best approaches to measurement. The objective of our study is to systematically review measures of social connection developed for LTC residents, including those with dementia, by evaluating their measurement properties including structural validity, internal consistency, reliability and construct validity. Method We are following COnsensus‐based Standards for the selection of health Measurement INstruments (COSMIN) systematic review methods. We searched multiple bibliographic databases to November 2021 for studies conducted in LTC residents, which quantified any aspect(s) of social connection, and reported at least one psychometric property for the measure(s) of social connection. We conducted a second targeted search in April 2022 using our list of identified measures, supplemented with a list of measures used in previous research in this population. We are using COSMIN guidelines to evaluate the measurement properties reported for each identified measure. Result We identified 68 studies reporting on 35 measures that assess social connection in LTC homes. Twenty‐five were measures of quality of life, wellbeing or life satisfaction and included a social connection subdomain. Only 10 measures specifically targeted social connection. From our pooled evaluation of 20 measures to date, we found that 20% (n = 4) have sufficient structural validity, 15% (n = 3) have sufficient internal consistency, 25% (n = 5) have sufficient reliability, and 15% (n = 3) have sufficient construct validity. Conclusion Many measures have assessed social connection in LTC settings for people living with dementia, but few are designed for this purpose and they often have insufficient psychometric properties. This review will provide detailed evidence of the quality of these measures to enable researchers to prioritize valid and reliable tools. Our results will inform our development of a new person‐centred social connection measurement tool for LTC residents in the Alzheimer’s Association/Brain Canada‐funded Social Connection in Long‐Term Care Home Residents (SONNET) study.
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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 | 0.018 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| 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".