Nurse-Led Virtual Delivery of PIECES in Canadian Long-Term Care Homes to Support the Care of Older Adults Experiencing Responsive Behaviors During COVID-19: Qualitative Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Worldwide, the COVID-19 pandemic has resulted in profound loss of life among older adults living in long-term care (LTC) homes. As a pandemic response, LTC homes enforced infection control processes, including isolating older adults in their rooms, canceling therapeutic programs, and restricting family member visits. Social isolation negatively impacts older adults in LTC, which may result in increased rates of anxiety, depression, physical and cognitive decline, disorientation, fear, apathy, and premature death. Isolation of older adults can also cause an increase in responsive behaviors (eg, yelling, hitting, calling out) to express frustration, fear, restricted movement, and boredom. To respond to the challenges in LTC and support frontline staff, older adults, and family members, a novel registered practical nurse (RPN)-led delivery of the PIECES approach for addressing responsive behaviors among older adults with dementia using virtual training/mentoring was implemented in Canadian LTC homes. PIECES employs a person- and family/care partner-centered collaborative team-based approach to provide education and capacity-building for nurses; engages families as active participants in care; and embeds evidence-informed practices to provide person- and family-centered care to older adults with complex needs, including dementia. OBJECTIVE: The aim of this study was to describe the experiences of LTC staff, family/care partners, and older adult research partners with implementation of a novel RPN-led virtual adaptation of the PIECES care-planning approach for responsive behaviors in two Canadian LTC homes during the COVID-19 pandemic. METHODS: Using a qualitative descriptive design, two focus groups were held with three to four staff members (eg, RPNs, managers) per LTC home in Ontario. A third focus group was held with three PIECES mentors. Individual semistructured interviews were conducted with RPN champions, family/care partners, and older adult research partners. Research team meeting notes provided an additional source of data. Content analysis was performed. RESULTS: A total of 22 participants took part in a focus group (n=11) or an in-depth individual interview (n=11). Participant experiences suggest that implementation of RPN-led virtual PIECES fostered individualized care, included family as partners in care, increased interdisciplinary collaboration, and improved staff practices. However, virtual PIECES, as delivered, lacked opportunities for family member feedback on older adult outcomes. Implementation facilitators included the provision of mentorship and leadership at all levels of implementation and suitable technological infrastructure. Barriers were related to availability and use of virtual communication technology (family members) and older adults became upset due to lack of comprehension during virtual care conferences. CONCLUSIONS: These findings offer promising support to adopting virtual PIECES, a team approach to gather valuable family input and engagement to address residents' unmet needs and responsive behaviors in LTC. Future research should investigate a hybridized communication format to foster sustainable person- and family-centered care-planning practices to include active collaboration of families in individualized care plans.
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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 | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".