“We Didn't Ask to Be Sick:” Equipping Residents and Care Partners for Transitions From Long‐Term Care to Hospital
Bibliographic record
Abstract
BACKGROUND: Patient-centered care is fundamental to high-quality healthcare, emphasizing respect for individual needs and preferences. In long-term care (LTC), this approach, known as resident-centered care, necessitates accommodating diverse levels of resident autonomy, particularly for those with cognitive impairments. Transitioning from LTC to hospital settings presents significant challenges, as it can disrupt continuity of care and heighten stress for residents, care partners, and staff. To address these challenges, decision-making should be thoughtful and planned rather than rushed or based on emotions during crises. METHODS: Following semi-structured needs assessment interviews with 28 participants from three LTC homes in Ontario, Canada, we organized focus groups with a subset of residents and care partners. These focus groups were conducted to gather insights and preferences for developing a decision-making tool tailored to LTC-to-hospital transitions. Using a framework designed to assess decisional needs and enhance decision outcomes, the focus groups explored participants' priorities and generated recommendations for the creation of a tool to test and evaluate in their LTC home. RESULTS: Participants proposed three tools to improve LTC-to-hospital transitions: (1) a decision-aid flowchart to guide users through questions, record details, and provide care options and contact information; (2) a hospitalization chapter in the LTC home's admission handbook with guidance, checklists, and cost breakdowns; and (3) a lanyard with a dual-sided card and QR code linking to an electronic platform containing important resident information and contact details. CONCLUSIONS: The study identified critical decisional needs and proposed tools that can address both preparation and decision-making aspects for hospital transitions from LTC. Co-designing these tools with residents and care partners ensures their alignment with user needs, enhancing resident-centered care during critical transitions. The implementation of these tools could significantly improve the transition experience, ensuring continuity of resident-centered care.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".