Communicating during care transitions for older hip fracture patients: family caregiver and health care provider's perspectives
Bibliographic record
Abstract
INTRODUCTION: Older hip fracture patients frequently require care across a variety of settings, from multiple individuals, including their family caregivers. We explored issues related to information sharing during transitional care for older hip fracture patients through the perspectives of both health care providers and family caregivers. METHODS: Thirty-five semi-structured interviews were conducted with family caregivers (n = 9) and health care providers (n = 26) of six hip fracture patients to gather perspectives on information sharing at each care transition, beginning with post-surgical discharge from acute care. Data were analysed using conventional qualitative content analysis methods using NVivo8 software. RESULTS: Both family caregivers and health care providers recognise that family caregivers' involvement has important benefits for patients, but this involvement is frequently limited by poor information sharing. Barriers include limited staff time, patient privacy regulations and lack of a clear structure to guide information sharing. Receiving, not offering, information was the focus of information sharing by both family caregivers and health care providers. CONCLUSIONS: Specific barriers that lead to poor information sharing between family caregivers and health care providers have been identified in this study. Possible interventions to improve information sharing include encouraging communication with family caregivers as standard care practice, educational strategies and more effective use of health information systems and technologies.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".