Video Conferencing With Residents and Families for Care Planning During COVID-19: Experiences in Canadian Long-Term Care
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Government-mandated health and safety restrictions to mitigate the effects of coronavirus disease 2019 (COVID-19) intensified challenges in caring for older adults in long-term care (LTC) without family/care partners. This article describes the experiences of a multidisciplinary research team in implementing an evidence-based intervention for family-centered, team-based, virtual care planning-PIECESTM approach-into clinical practice. We highlight challenges and considerations for implementation science to support care practices for older adults in LTC, their families, and the workforce. RESEARCH DESIGN AND METHODS: A qualitative descriptive design was used. Data included meetings with LTC directors and Registered Practical Nurses (i.e., licensed nurse who graduated with a 2-year diploma program that allows them to provide basic nursing care); one-on-one interviews with family/care partners, residents, Registered Practical Nurses, and PIECES mentors; and reflections of the academic team. The Consolidated Framework for Implementation Research provided sensitizing constructs for deductive coding, while an inductive approach also allowed themes to emerge. RESULTS: Findings highlighted how aspects related to planning, engagement, execution, reflection, and evaluation influenced the implementation process from the perspectives of stakeholders. Involving expert partners on the research team to bridge research and practice, developing relationships from a distance, empowering frontline champions, and adapting to challenging circumstances led to shared commitments for intervention success. DISCUSSION AND IMPLICATIONS: Lessons learned include the significance of stakeholder involvement throughout all research activities, the importance of clarity around expectations of all team members, and the consequence of readiness for implementation with respect to circumstances (e.g., COVID-19) and capacity for change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".