Facilitating family‐focused Care of Older adults living in <scp>Long‐Term</scp> Care in Canada during Restricted Visiting due to <scp>COVID</scp>‐19
Bibliographic record
Abstract
BACKGROUND: The focus of this paper is exemplary gerontological nursing interventions that effectively supported families and long-term care residents in Canada during visiting restrictions resulting from COVID-19. OBJECTIVE: The aim was to describe exemplary gerontological nursing interventions that families and long-term care residents in Canada found supportive during visiting restrictions resulting from COVID-19. METHODS: An analysis of data artefacts including news reports, blogs and social media postings was completed. RESULTS: Thematic analysis resulted in four themes: dedication amidst challenge, innovation and continuous learning, living their nursing values and purposeful knowledge sharing. These themes are described using a framework that depicts four pillars of exemplary nursing practice: professionalism, scholarly practice, leadership and stewardship (Riley, Beal, & Ponte, 2021). CONCLUSIONS/IMPLICATIONS FOR PRACTICE: A link is made between these pillars of exemplary practice and enactment of family-focused care. Recommendations focused on gerontological nursing approaches that facilitate family-focused care for older adults residing in long-term care are included.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".