Social Capitalization Through Information and Communication Technologies to Reduce Social Isolation and Loneliness in Long-Term Care Homes residents during the COVID-19 epidemic: a Systematic Review of Published Research
Bibliographic record
Abstract
Title Social Capitalization Through Information and Communication Technologies to Reduce Social Isolation and Loneliness in Long-Term Care Homes residents during the COVID-19 epidemic: a Systematic Review of Published Research Authors : Idrissa Beogo* Eric Nguemeleu Tchouaket, Sia Drissa, Marie-Pierre Gagnon *Professeur adjoint / Assistant Professor École des sciences infirmières / School of Nursing Faculté des sciences de la santé / Faculty of Health Sciences Université d'Ottawa / University of Ottawa Pavillon Roger-Guindon / Roger-Guindon Hall 451, chemin Smyth Road, Pièce 3236A / Room 3236A Ottawa, Ontario, Canada K1H 8M5 Objective To synthesize the effects of Information and Communication Technologies interventions to reduce social isolation and loneliness in long-term care homes residents during the COVID-19 period Background The current COVID-19 pandemic has heightened the risk of social isolation and loneliness in Long-Term Care Homes (LTCHs) residents. Besides, the high mortality borne, surviving residents have to cope with social isolation and loneliness. That is, we have witnessed an overwhelm use of all kinds of information and communication technology (ICT) such as FaceTime or Skype apps by families to keep in touch with their loved. But, so far, these effects have not been synthesized in the recent literature. Our project intends to summarize evidence on the effectiveness of ICT interventions utilized over the Covid-19 pandemic to capitalize the social connection in replacement of the face-to-face format formerly used by families before the pandemic. Methods: The research covers the Covid-19 pandemic period, from 20219 to now. Published literature will be screened from nine electronic databases to include french, english, and chinese papers. Only studies dealing Long-term Care Homes will be consider for inclusion. Discussion/Conclusion: Findings will help find out the effect of added value of ICT in SIL reduction in LTCHs. Our findings will support the development of policy regarding the ICT interventions in LTCHs in our continuously aging society. Funding The Canadian Institutes of Health Research (CIHR) Search Strategy The search strategy included both published through a three-stage search strategy. 1.An initial exploratory search in Medline, as a test, to capture titles and abstracts, using free vocabulary, keywords and MeSH term adjusted to each database. 2.Then, the search strings will be well-calibrated after refined iterative processes to improve the likelihood of retrieving relevant articles. Finally, the validated search equation will be performed in electronic databases. Information Sources (describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and dates you will or last searched) Nine electronic databases are targeted : OvidMedline®, CINHAL, Cochrane Library, Embase, Web of Science, PsycINFO, Communication & Mass Media Complete, Association for Computing Machinery (ACM) Digital Library, and IEEE Xplore. This will be supplemented with a manual search and the authors will be approached for further details if needed. The time frame covered the Covid-19 onset to now (2019- December 2021). Inclusion Criteria Older adults ≥ 65 years in LTCH Exclusion Criteria - Persons with severe neurocognitive disorders and - Persons with hearing impaired Condition, disease or problem being studied Social Isolation and Loneliness in Long-Term Care Homes residents during the COVID-19 epidemic Patients/Participants/Population Long-Term Care Homes residents Intervention(s) or Exposure(s) All kind of ICT intervention using audio and/or video or chat apps (ex. Skype, Windows Live Messenger or Facebook) Control or Comparator(s) No comparator Primary Outcomes - SIL level measured by any appropriate and validated tool - Proxy outcomes measured SIL level 1. Companionship, 2. Friendship, 3. Feeling of being forgotten and not belonging 4. Connection with family Data extraction (selection and coding) The references will be imported into Rayyan reference screening system. The PI (Idrissa Beogo [IB]) and the research assistant (Junqiang Zhao [JZ], PhD student) will independently screened all titles and abstracts for potential inclusion. Discrepancies will be resolved by Professor Sia Drissa and Professor Eric Tchouacket N. Thereafter, JZ will go on the extraction after a pilot extraction with IB, using using an Excel spreadsheet. Risk of bias (quality) assessment We will employ the Cochrane Risk of Bias (ROB) Data Synthesis Strategy Narrative synthesis Quantitative synthesis and conduct a meta-analysis if possible Analysis of subgroups or subsets By type of ICT used By type of LTCHs By The Covid-19 epidemic waves Dissemination plan Publication in peer-review journal Local and international conferences Workshops organized by the Canadian Institutes of Health Research Stage of Review at Time of Submission Protocol Written
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".