Digital Technologies To Enhance Social Connectedness in Long-Term Care Facilities During COVID-19: A Review
Bibliographic record
Abstract
Abstract A consequence of the strict visitor restrictions implemented by many Long-term Care Facilities (LTCFs), during the COVID-19 pandemic, was the exacerbation of loneliness and social isolation felt by older adult residents. While there had been a shift by some persons to utilize digital solutions to mitigate the effects of the imposed social isolation, many facilities did not have sufficient information regarding available solutions to implement institutional strategies to support social connectedness through digital solutions. To support our partners in evidence-based policy-making we conducted a scoping review to identify existing virtual technology solutions, apps, and platforms feasible to promote social connectedness among persons residing in a long-term care facility context during times of lockdown such as experienced during the COVID-19 pandemic. Initial identification of relevant literature involved a combination of keywords and subject headings searches within 5 databases (PubMed, CINAHL EBSCO, PsychINFO EBSCO, Embase OVIDSP, and Web of Science ISI). DistillerSR was used to screen, chart and summarize the data. There is growth in the availability of technologies focused on promoting health and well-being in later life for persons in long-term care facilities however a gap remains in widespread uptake. We will describe the breadth of technologies identified in this review and discuss how they vary in utility in smaller scale facilities common in rural areas. Of the technologies that can be used to mitigate the impacts of social isolation felt by long-term care residents, many “solutions” depend on stable highspeed internet, which remains a challenge in rural and northern areas.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".