Resident- and family-led huddles for collaborative care planning in long-term care: a feasibility study
Bibliographic record
Abstract
Background and Objectives: Engaging long-term care residents and families in decisions and as active partners in care can improve the quality of care. However, barriers to effective communication among the care team, residents, and families remain. This study aimed to assess the feasibility, acceptability, and satisfaction with an intervention to engage residents, their family, and team members in a collaborative approach to care planning to support person- and relationship-centered care. Research Design and Methods: A multi-method approach was utilized. The intervention included: leadership coaching sessions; an education session with team members and leaders for communication skills training; and resident- and family-led care planning conversations (huddles) that included huddle training. Results: The intervention was found to be feasible to implement and was acceptable to participants. The leadership coaching sessions and the huddles could be feasibly conducted in-person or virtually. Participants were satisfied with the leadership coaching and the huddles, and the communication tool was found to be useful. Suggestions were made to further innovate huddles. Discussion and Implications: Huddles provided a structured approach and relational process that facilitated communication among team members, residents, and families. The communication tool provided a common language for discussing resident care. The leadership coaching sessions and huddles were feasible to implement and can be adapted for virtual delivery. Huddles are a promising practice for engaging residents and families in care planning, but further testing is needed. Clinical Trial Registration: NCT04026698.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".