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Record W4411713030 · doi:10.51731/cjht.2025.1155

Alternate Level of Care in Canada: Evidence Assessment Report

2025· article· en· W4411713030 on OpenAlexaboutno aff
CDA-AMC

Bibliographic record

VenueCanadian Journal of Health Technologies · 2025
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMedicineHistoryPolitical science

Abstract

fetched live from OpenAlex

Alternate level of care (ALC) is a designation used in Canada that is applied by clinical staff to that portion of a patient’s hospital stay when the patient is occupying a bed in a facility (e.g., acute care hospital) and does not require the intensity of resources or services typically provided in that care setting. (In other parts of the world ALC is often referred to as delayed discharge.) There are several reasons that patients who have been designated as ALC continue to occupy a hospital care bed or use hospital resources. People may present to a hospital emergency department for nonacute medical or social reasons because of a real, or perceived, lack of access to more appropriate services (e.g., primary care, community supports). Patients may require new or additional services and be waiting for availability, such as home care or specialized care. To address the challenges associated with ALC designations, we explored strategies and initiatives that are being considered or implemented to enhance the infrastructure and support systems aimed at reducing ALC rates in Canada. This work builds on a previous request from a health care decision-maker for a report describing how health care centres or regions in Canada are handling ALC and any strategies they have found that successfully decrease hospital wait times or length of stay and increase patient flow. This work also builds on previous health systems work undertaken by Canada’s Drug Agency, including emergency department overcrowding and aging in place.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.137
GPT teacher head0.450
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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