Social connection in long‐term care home residents: A review of research on health outcomes and strategies during COVID‐19
Bibliographic record
Abstract
Abstract Background The infection control measures enacted to prevent COVID‐19 in long‐term care (LTC) homes have highlighted the important role of social connection in the wellbeing and care of people living in these settings. Yet, issues of loneliness and social isolation in LTC home residents pre‐date COVID‐19, as does the research to address them. Method We conducted a scoping review of published research that quantified the association of any aspect of social connection among LTC residents with mental health outcomes. We also sought studies of modifiable risk factors and interventions that identified strategies that could be implemented and adapted by LTC residents, families and staff during COVID‐19. We searched eight databases from inception to search date (July 2019), extracted data and conducted a narrative synthesis of evidence. We involved knowledge users representing LTC residents, families and staff in priority‐setting (defining the review questions), analyzing data, interpreting and contextualizing the results, and disseminating the findings. In this presentation, to further characterize the evidence, we compare the measures used in these studies to those identified from an ongoing systematic review of measures developed specifically to assess social connection in people living in LTC homes. Result We located 61 studies that reported the association between social connection and mental health outcomes, including depression; responsive behaviors; mood, affect, and emotions; anxiety; medication use; cognitive decline; and, others. We located 72 studies that informed 12 strategies for building and maintaining social connection during COVID‐19; some strategies represented fundamental aspects of resident care whereas others that would need to be considered in context with a LTC resident’s and home’s needs and circumstances. Conclusion Our review identifies a body of research on social connection in LTC that pre‐dates the COVID‐19 pandemic. The evidence points to quality of social connection for residents being associated with better mental health, and we identified strategies that may help to build and maintain social connection in this setting. We found that the studies assessed numerous aspects of social connection, however, only some used measures developed specifically for this population.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".