Setting the balance of care for older adults at risk of hospitalization and delayed discharge: A mixed-methods research protocol
Bibliographic record
Abstract
INTRODUCTION: Delayed hospital discharge is a persistent care quality issue experienced across health systems worldwide and remains a priority area to be addressed in Canada. Often associated with a decrease in services while waiting to leave the hospital, delayed discharge from hospital can lead to increased frailty, physical and cognitive decline, and caregiver burnout. Optimizing availability of and timely access to community-based health and social care are avenues that could reduce initial admissions to the hospital and length of hospital stay, and facilitate hospital discharges. METHODS: This research will explore the ways in which community resources could be leveraged to potentially avoid hospitalization and delayed hospital discharge for older adults using sequential mixed-methods including co-design. To better understand the characteristics and needs of older adults, the research team will first identify sub-populations of older adults (65 years old or older) at risk of hospitalization and delayed discharge using comprehensive, longitudinal administrative health data. From these health data, risk profiles and personas will be created and then shared with key partners (e.g., older adults, caregivers, healthcare providers, healthcare decision-makers), who will be engaged to identify, leverage, and create targeted care solutions. The barriers and facilitators to the implementation of these care solutions will then be explored. DISCUSSION: Delayed hospital discharge has been a critical care quality issue across Canada for decades. The current research will provide health system leaders with an approach to better allocate services to older adults in order to avoid delayed hospital discharge and identify gaps in health and social care resources based on the characteristics, needs, and preferences of older adults, their caregivers, and providers.
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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 | 0.084 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.009 |
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".