Meeting the needs of older adults living in long‐term care homes through co‐design of a Virtual Care Planning ToolKit
Bibliographic record
Abstract
BACKGROUND: Health/safety restrictions to control COVID-19 in long-term care (LTC) homes were very challenging for residents, their families, administrators, and staff. Mental and physical well-being of residents declined with the imposed social isolation, workforce shortages, and loss of informal care provision by family/care partners. Unmet needs of older adults living with dementia manifest as behavioural expressions (e.g., irritable, agitated or aggressive behaviour). The [PIECES approach] (Physical, Intellectual, Emotional health, maximizing individual Capabilities for quality of life, living Environment and Social including a person's beliefs, culture, and life story) offers an evidence-based framework to address behavioural expressions through team collaboration. Building on our previous work addressing virtual application of the PIECES approach with Registered Practical Nurses (RPNs) leading as PIECES champions in LTC homes, this study aimed to co-design a ToolKit (i.e., website, resources, infographics) with the collaboration of residents, their family/care partners, RPNs, administrators, PIECES mentors, and Behavioural Supports Ontario (BSO) Leads. The ToolKit will contain resources supporting virtual care planning including the PIECES approach. METHOD: This study employed a qualitative descriptive design. Four workshop sessions with two LTC homes in Ontario, Canada were held with residents, family/care partners, RPNs, and BSO Leads participating to co-design a virtual ToolKit including the PIECES approach, and provide recommendations. Study collaborators attended sessions in person or by videoconferencing with the research team and PIECES mentors. RESULT: Recommendations for the information and web presence of the PIECES ToolKit included greater inclusion of infographics, embedded short video clips, reduced text, examples of communication memos and telephone scripts for engaging families, options to have the web page content read aloud, and practical user-friendly tips to support communication technology (e.g., ZOOM) and preparation suggestions for virtual engagement for care planning. Further, separate sections specific to staff and family were advocated to meet unique needs, with limited available time, a range in familiarity with navigating websites, and improved to-the-point information. CONCLUSION: This project delivers an online accessible open resource available to guide staff and family in preparing for and implementing virtual team-based care planning engaging family/care partners using the PIECES framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".