Improving In-Hospital Care For Older Adults: A Mixed Methods Study Protocol to Evaluate a System-Wide Sub-Acute Care Intervention in Canada
Bibliographic record
Abstract
Introduction: Acute care hospitals often inadequately prepare older adults to transition back to the community. Interventions that seek to improve this transition process are usually evaluated using healthcare use outcomes (e.g., hospital re-visit rates) only, and do not gather provider and patient perspectives about strategies to better integrate care. This protocol describes how we will use complementary research approaches to evaluate an in-hospital sub-acute care (SAC) intervention, designed to better prepare and transition older adults home. Methods: In three sequential research phases, we will assess (1) SAC transition pathways and effectiveness using administrative data, (2) provider fidelity to SAC core practices using chart audits, and (3) SAC implementation outcomes (e.g., facilitators and barriers to success, strategies to better integrate care) using provider and patient interviews. Results: Findings from each phase will be combined to determine SAC effectiveness and efficiency; to assess intervention components and implementation processes that 'work' or require modification; and to identify provider and patient suggestions for improving care integration, both while patients are hospitalized and to some extent after they transition back home. Discussion: This protocol helps to establish a blueprint for comprehensively evaluating interventions conducted in complex care settings using complementary research approaches and data sources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".