Bibliographic record
Abstract
What Is the Issue? Alternate level of care (ALC) is when a patient is occupying a bed in a hospital and does not require the intensity of resources or services provided in that hospital. ALC is a persistent barrier to providing efficient health care in Canada, as it is in most health systems worldwide (where ALC is referred to as delayed discharge). Older adults (aged 65 years or older) who require placement in residential care are the largest subgroup of the ALC patient population. Analyzing ALC use data can inform decision-makers about data trends and which jurisdictions have reduced ALC times. By understanding the strategies, policies, or other interventions that have been used to reduce ALC in Canada, and which have been successful at minimizing ALC, decision-makers can consider which strategies to implement in their health jurisdictions. What Did We Do? We analyzed Canadian Institute for Health Information data related to ALC and average length of ALC in older adults in the provinces and territories of Canada. We conducted an environmental scan of the academic and grey literature to: identify strategies to address ALC in older adults in Canada identify strategies that have been effective in reducing ALC in older adults in Canada. What Did We Find? In 2022 to 2023, Canada (excluding Quebec) had 15 ALC hospitalizations per 1,000 population, 369 total ALC days per 1,000 population, and a mean of 25 ALC days per hospitalization in patients aged 65 and older awaiting admission to residential care or elsewhere. While there were variations across jurisdictions, the trends in ALC over time for adults aged 55 years and older were relatively consistent. Patients with more ALC days were aged 75 years and older, had lower incomes, and were admitted to the hospital as urgent. We identified 19 strategies that addressed ALC in older adults in Canada. These included input, throughput, and system-level interventions, which we categorized as live information sharing, recommended initiatives, tools and guidelines, practice changes, and infrastructure and finance. We identified 4 studies that reported a favourable effect of a throughput or system-level strategy compared to no strategy or standard care on ALC hospitalizations, length of stay, or discharge to home. Two throughput strategies may be effective: The Subacute Care for the Frail Elderly (SAFE) Unit improved ALC length of stay (LOS), hospital LOS, and discharge to home. The Transitional Care Unit improved discharge to home. Two system-level strategies may be effective: Home First improved ALC hospitalizations, ALC LOS, and discharge to home. Behavioural Supports Ontario improved ALC hospitalizations and ALC LOS. What Does This Mean? We found common themes in our environmental scan that decision-makers may incorporate into strategies for addressing ALC in older adults waiting for residential care, including the provision of integrated care, promotion of age-friendly care, early identification of patients at risk of ALC, sharing of tools and resources, transitional care, and inclusion of families and caregivers in care planning. This report may serve as the first step for future systematic reviews or other evidence syntheses with a broader scope. Future research might investigate the factors that contribute to ALC and interventions to address those factors.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".