Information sharing with rural family caregivers during care transitions of hip fracture patients
Bibliographic record
Abstract
INTRODUCTION: Following hip fracture surgery, patients often experience multiple transitions through different care settings, with resultant challenges to the quality and continuity of patient care. Family caregivers can play a key role in these transitions, but are often poorly engaged in the process. We aimed to: (1) examine the characteristics of the family caregivers' experience of communication and information sharing and (2) identify facilitators and barriers of effective information sharing among patients, family caregivers and health care providers. METHODS: Using an ethnographic approach, we followed 11 post-surgical hip fracture patients through subsequent care transitions in rural Ontario; in-depth interviews were conducted with patients, family caregivers (n = 8) and health care providers (n = 24). RESULTS: Priority areas for improved information sharing relate to trust and respect, involvement, and information needs and expectations; facilitators and barriers included prior health care experience, trusting relationships and the rural setting. CONCLUSION: As with knowledge translation, effective strategies to improve information sharing and care continuity for older patients with chronic illness may be those that involve active facilitation of an on-going partnership that respects the knowledge of all those involved.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".