“The first transition is from resident to patient”: understanding the decisional needs of long-term care residents preparing for hospital transitions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Long-term care (LTC) residents are frequently transferred to hospitals, yet these transitions do not always align with residents' care goals or preferences. This study aimed to assess the decisional and informational needs of LTC residents, focusing on their decision-making priorities and knowledge gaps when facing hospital transition decisions. RESEARCH DESIGN AND METHODS: The researchers conducted 28 semi-structured interviews with residents (n = 9), care partners (n = 8), and staff (n = 11) across three LTC homes in Ontario, Canada. Guided by the Ottawa Decision Support Framework and the Decisional Needs Assessment Workbook, the interviews explored participants' experiences, decision-making needs, and information requirements for LTC-to-hospital transitions. Interviews were transcribed verbatim and analyzed using reflexive thematic analysis. RESULTS: Participants were predominantly white and female, with 68.3 as residents' mean age and 70.5 as care partners' mean age. Staff (mean age: 46.5) were more ethnically diverse in a range of clinical and leadership roles. Four interconnected themes about resident needs during LTC-to-hospital transitions emerged: (a) communication and transparency, (b) continuity of care between LTC homes and hospitals, (c) awareness of ageist assumptions and conflicting priorities, and (d) trust building during care transitions. DISCUSSION AND IMPLICATIONS: This study highlights the persistent challenges that residents, care partners, and staff face in preparing for and making decisions regarding LTC-to-hospital transitions. The findings emphasize the urgent need for decision-support tools that empower LTC residents to feel better prepared to make decisions that reflect their values, priorities, and goals of 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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".