Technologies and the Effects On Social Engagement In Long-Term Care Facilities During COVID-19: A Scoping Review
Bibliographic record
Abstract
Abstract During the COVID-19 pandemic, the sense of loneliness and social isolation felt by older adults in long-term care facilities has been exacerbated. Although there has been an increase in the number of digital solutions to mitigate social isolation during COVID-19, facilities in northern British Columbia do not have sufficient information regarding the technologies to support social connectedness. To support evidence-based policy decisions, a scoping review was conducted to identify existing virtual technology solutions, apps, and platforms that promote social connectedness among older adults residing in long-term care. A combination of keywords and subject headings were used to identify relevant literature within PubMed, CINAHL EBSCO, PsychINFO EBSCO, Embase OVIDSP, and Web of Science ISI databases. DistillerSR was used to screen and summarize the article selection process. Twenty-three articles were identified for full-text analysis. A variety of technologies are described which can be used to mitigate the impacts of social isolation felt by long-term care residents. However, many of these digital solutions require stable highspeed internet. This remains a challenge for facilities in northern areas as many have limited access to reliable internet. Metrics used to evaluate social engagement in the context of long-term care are also outlined. This research provides the preliminary groundwork necessary to better inform policy decisions about which technologies are available and, of these, which are effective at enhancing social connectedness for older adults in long-term care.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".