Social Connectivity in the Context of COVID-19 and Long-Term Care
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has disproportionately impacted older adults, particularly those residing in long-term care homes (LTCHs), causing immense loss of life and resulting in overall health declines in LTCH residents. These vulnerable older adults have also experienced extreme loneliness, anxiety and depression. Social connectedness is an important contributor to well-being and quality of life of older adults in LTCHs and family members are an essential component to this. However, restrictions driven by policies to protect resident safety, have constrained family members’ access to long-term care homes and limited in-person contact between residents and their families. In their absence, health providers have been integral to supporting connections between residents and their families within LTCHs. This study aimed to understand the experiences of social connectedness between residents and family members who have been physically separated due to the current pandemic and, to examine LTCH health providers’ experiences and responses to support social connectedness. Using a qualitative descriptive design, in-depth semi-structured interviews were conducted with 21 family members and 11 healthcare providers. Emergent themes from qualitative content analysis are: (a) all-encompassing impacts of separation; (b) advocacy became my life; (c) the emotional toll of the unknown; 4) the burden of information translation; 5) precarious balance between safety and mistrust for the healthcare system; and (d) a formulaic approach impedes connectivity. A more comprehensive understanding of the experiences and support needs of LTCH residents and their family members within the context of a pandemic can inform practice approaches to support social connections going forwards.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".