Integrated transitional care: patient, informal caregiver and health care provider perspectives on care transitions for older persons with hip fracture
Bibliographic record
Abstract
INTRODUCTION: Complex older adults, such as those with hip fracture, frequently require care from multiple professionals across a variety of settings. Integrated care both between providers and across settings is important to ensure care quality and patient safety. The purpose of this study was to determine the core factors related to poorly integrated care when hip fracture patients transition between care settings. METHODS: A qualitative, focused ethnographic approach was used to guide data collection and analysis. Patients, their informal caregivers and health care providers were interviewed and observed at each care transition. A total of 45 individual interviews were conducted. Interview transcripts and field notes were coded and analysed to uncover emerging themes in the data. RESULTS: FOUR FACTORS RELATED TO POORLY INTEGRATED TRANSITIONAL CARE WERE IDENTIFIED: confusion with communication about care, unclear roles and responsibilities, diluted personal ownership over care, and role strain due to system constraints. CONCLUSIONS: Our research supports a broader notion of collaborative practice that extends beyond specific care settings and includes an appropriate, informed role for patients and informal caregivers. This research can help guide system-level and setting-specific interventions designed to promote high-quality, patient-centred care during care transitions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".