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Record W4200517076 · doi:10.2196/preprints.33610

Co-Development of a Web-Based Hub (eSocial-hub) to Combat Social Isolation and Loneliness in Francophone and Anglophone Older People in the Linguistic Minority Context (Quebec, Manitoba, and New Brunswick): Protocol for a Mixed Methods Interventional Study (Preprint)

2021· preprint· en· W4200517076 on OpenAlexaboutno aff
Idrissa Beogo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsLonelinessSocial isolationContext (archaeology)Isolation (microbiology)FrenchPandemicSocial mediaSociologyNursingPolitical sciencePsychologyPublic relationsMedicineCoronavirus disease 2019 (COVID-19)GeographySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND The first wave of the COVID-19 pandemic has severely hit Canadian nursing facilities (81% of deaths). To this toll, public health measures (eg, visitation restriction) have subsequently deepened the social isolation and loneliness of residents in nursing facilities (NFs), especially those in linguistic minority settings: Anglophone institutions in Quebec and Francophone institutions outside Quebec. However, very few COVID-19 initiatives targeting these populations specifically have been documented. Given the limited number of NFs serving linguistic minorities in Canadian populations, families and loved ones often live far from these facilities, sometimes even in other provinces. This context places the digital solutions as particularly relevant for the present COVID-19 pandemic as well as in the post–COVID-19 era. OBJECTIVE This project aims to co-develop a virtual community of practice through a web-based platform (eSocial-hub) to combat social isolation and loneliness among the older people in linguistic minority settings in Canada. METHODS An interventional study using a sequential mixed methods design will be conducted. Four purposely selected NFs will be included, 2 among facilities in Manitoba and 2 in New Brunswick; and 2 Anglophone NFs in Quebec will serve as knowledge users. The development of eSocial-hub will include an experimental 4-month phase involving the following end users: (1) older people (n=3 per NF), (2) families of the participating older people (n=3 per NF), and (3) frontline staff (nurse and health care aid; n=2 per NF). RESULTS Activities and solutions aiming at reducing social isolation and loneliness will be implemented and then evaluated with the project stakeholders, and the best practices generated. The assessment will be conducted using indicators derived from the 5 domains of the Consolidated Framework for Implementation Research. The project will be led by an interdisciplinary team and will involve a multisectoral partnership. CONCLUSIONS The project will develop a promising and generalizable solution that uses virtual technology to help reduce social isolation and loneliness among the older people.

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 imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.988
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.028
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.002
Science and technology studies0.0060.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0460.007

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.

Opus teacher head0.145
GPT teacher head0.512
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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