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
Bibliographic record
Abstract
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. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/30802.
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".