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Enregistrement W6962623624 · doi:10.17605/osf.io/sjhpb

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

2021· article· en· W6962623624 sur OpenAlexaboutno aff

Notice bibliographique

RevueOSF Preprints (OSF Preprints) · 2021
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueTechnology Use by Older Adults
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLonelinessSocial isolationPsychological interventionIsolation (microbiology)Information and Communications TechnologyPandemicSocial mediaSystematic reviewHealth care

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,045
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,443
Score d'incertitude au seuil0,963

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0090,045
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,001
Science ouverte0,0010,002
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,039
Tête enseignante GPT0,380
Écart entre enseignants0,342 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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