Strengthening Social Capital to Address Isolation and Loneliness in Long-term Care Facilities During the COVID-19 Pandemic: Protocol for a Systematic Review of Research on Information and Communication Technologies
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had the greatest impact in long-term care facilities (LTCFs) by disproportionately harming older adults and heightening social isolation and loneliness (SIL). Living in close quarters with others and in need of around-the-clock assistance, interactions with older adults, which were previously in person, have been replaced by virtual chatting using information and communication technologies (ICTs). ICT applications such as FaceTime, Zoom, and Microsoft Teams video chatting have been overwhelmingly used by families to maintain residents' social capital and subsequently reduce their SIL. OBJECTIVE: Because of the lack of substantive knowledge on this ever-increasing form of social communication, this systematic review intends to synthesize the effects of ICT interventions to address SIL among residents in LTCFs during the COVID-19 period. METHODS: We will include studies published in Chinese, English, and French from December 2019 onwards. Beyond the traditional search strategy approach, 4 of the 12 electronic databases to be queried will be in Chinese. We will include quantitative and intervention studies as well as qualitative and mixed methods designs. Using a 2-person approach, the principal investigator and one author will blindly screen eligible articles, extract data, and assess risk of bias. In order to improve the first round of screening, a pilot-tested algorithm will be used. Disagreements will be resolved through discussion with a third author. Results will be presented as structured summaries of the included studies. We plan to conduct a meta-analysis if sufficient data are available. RESULTS: A total of 1803 articles have been retrieved to date. Queries of the Chinese databases are ongoing. The systematic review and subsequent manuscript will be completed by the fall of 2022. CONCLUSIONS: ICT applications have become a promising avenue to reduce SIL by providing a way to maintain communication between LTCF residents and their families and will certainly remain in the post-COVID-19 period. This review will investigate and describe context-pertinent and high-quality programs and initiatives to inform, at the macro level, policy makers and researchers, frontline managers, and families. These methods will remain relevant in the post-COVID-19 era. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36269.
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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 | 0.085 | 0.101 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.024 | 0.021 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.063 | 0.009 |
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".