ENHANCING CARE AND TEAM PROCESSES THROUGH NURSE PRACTITIONER-LED HUDDLES IN LONG-TERM CARE HOMES
Bibliographic record
Abstract
Abstract Long-term care (LTC) homes faced unprecedented challenges during the COVID-19 pandemic, impacting both staff and residents. Huddles led by a skilled facilitator have the potential to enhance care and team dynamics positively. Nurse practitioners (NPs) are highly trained clinician and leader with a proven history of contributing to favorable outcomes for residents, staff, and the health system within LTC homes. The objectives of this mixed-methods study included: 1) assess the implementation of NP-led huddles; 2) compare outcomes of moral distress and perceived support between staff; and 3) examine changes in resident outcomes. Over a four-month period, huddles were conducted on two units of a privately-owned, not-for-profit LTC home in Ontario, Canada. Post-implementation outcomes were compared between staff who attended at least one huddle (intervention group, n=20) and those who did not (control group, n=22). Anonymized resident-level data from RAI MDS 2.0 were utilized to evaluate resident outcomes. Bayesian analysis was employed to compare outcomes across different staff categories and to summarize changes in RAI measures before and after the intervention. Forty-eight huddles, primarily focusing on resident care (46%) and staff well-being (34%), were conducted by the NP. Direct care staff who attended huddles reported lower levels of moral distress and increased support from the NP. One of the intervention units showed statistical evidence of reduced medical complexity among residents over time. In conclusion, NP-led huddles have the potential to positively impact both staff and resident outcomes and integration of NPs into LTC can facilitate implementation of evidence-informed practices in LTC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".