Learnings From a Novel Virtual Care Planning Intervention Targeting Registered Practical Nurses in Long-Term Care Homes During COVID-19
Bibliographic record
Abstract
Introduction: The PIECES approach has been utilized for over 25 years across various Canadian healthcare settings, including long-term care (LTC). PIECES fosters a team-based, person-centred approach to addressing responsive behaviors—such as yelling and restlessness—often linked to unmet personal needs. Objective: This study aimed to explore, with implementation of the virtual version of PIECES: (a) the experiences of LTC staff, focusing on challenges, facilitators, and recommendations; and (b) resilience and interprofessional collaboration among LTC staff. Methods: A convergent mixed method approach used focus groups with registered practical nurses (RPNs), managers, PIECES-trained RPN champions and PIECES mentors to gather their experiences. Surveys at baseline and post-intervention assessed individual and workplace resilience, and team collaboration. Results: Themes identified through reflexive thematic analysis included increased team collaboration and efficacy to manage responsive behaviors through PIECES care planning. Formalized processes supported by leadership with input from family/care partners enhanced nurses’ ability to provide the needed care for responsive behaviors. Primary challenges to PIECES implementation were unfamiliarity with technology and staff shortage. Recommendations included embedding technology in usual care, ongoing support with referral process and continued virtual PIECES training. Standard outcome measures revealed reduced personal and workplace resilience, and team collaboration. Discussion and Conclusion: The RPN-led referral algorithm for the virtual PIECES approach invested the LTC staff together during the difficult COVID period and challenged their existing skills and knowledge of technology.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".