A FRAMEWORK FOR CARE TRANSITIONS FOR OLDER ADULTS WITH COMPLEX HEALTH CONDITIONS
Bibliographic record
Abstract
Abstract For older adults with complex health conditions, transitions between care settings are common and a major risk to quality of care and patient safety. Care transition interventions have shown positive impacts on continuity of care and health service use, however, most require additional human resources (e.g., transition coach), focus on one transition or “handoff”, and provide support for individual patients without addressing underlying challenges of health system integration. We sought to develop a framework for system-level enhancements to care transitions for older adults. We report a secondary framework analysis of an ethnographic investigation (the “InfoRehab” project) of care transitions for older persons who had experienced a hip fracture. The ethnographic study involved interviews, observations, and document reviews for 23 patients, 19 family caregivers, and 92 health care providers. Data were collected at each transition point (1-4/patient) along the care continuum, at three Canadian sites (large urban, mid-size urban, rural). Our framework analysis followed the approach described by Gale et al. (2013), using as cases 12 peer-reviewed papers which had reported InfoRehab results. Two researchers coded findings from each paper, then developed an analytical framework of eight themes by consensus; these include: patient involvement and choice, family caregiver involvement, patient complexity, health care provider coordination, information sharing, documentation, system constraints, and relationships. NVivo 11 was used to index findings into these themes and to generate a matrix. We are working with system stakeholders, including patients and caregivers, to apply this framework in the development of improved systems for care transitions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".