Social Connection in Long-Term Care Homes: A Scoping Review of Published Research on the Mental Health Impacts and Potential Strategies During COVID-19
Bibliographic record
Abstract
OBJECTIVES: Good social connection is associated with better health and wellbeing. However, social connection has distinct considerations for people living in long-term care (LTC) homes. The objective of this scoping review was to summarize research literature linking social connection to mental health outcomes, specifically among LTC residents, as well as research to identify strategies to help build and maintain social connection in this population during COVID-19. DESIGN: Scoping review. SETTINGS AND PARTICIPANTS: Residents of LTC homes, care homes, and nursing homes. METHODS: We searched MEDLINE(R) ALL (Ovid), CINAHL (EBSCO), PsycINFO (Ovid), Scopus, Sociological Abstracts (ProQuest), Embase and Embase Classic (Ovid), Emcare Nursing (Ovid), and AgeLine (EBSCO) for research that quantified an aspect of social connection among LTC residents; we limited searches to English-language articles published from database inception to search date (July 2019). For the current analysis, we included studies that reported (1) the association between social connection and a mental health outcome, (2) the association between a modifiable risk factor and social connection, or (3) intervention studies with social connection as an outcome. From studies in (2) and (3), we identified strategies that could be implemented and adapted by LTC residents, families and staff during COVID-19 and included the articles that informed these strategies. RESULTS: We included 133 studies in our review. We found 61 studies that tested the association between social connection and a mental health outcome. We highlighted 12 strategies, informed by 72 observational and intervention studies, that might help LTC residents, families, and staff build and maintain social connection for LTC residents. CONCLUSIONS AND IMPLICATIONS: Published research conducted among LTC residents has linked good social connection to better mental health outcomes. Observational and intervention studies provide some evidence on approaches to address social connection in this population. Although further research is needed, it does not obviate the need to act given the sudden and severe impact of COVID-19 on social connection in LTC residents.
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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 | 0.024 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".