A Cross-Sectional Descriptive Study Using Health Administrative Data to Examine the Characteristics of Older Adults Incurring Delayed Discharge Days for Non-Clinical Reasons During the COVID-19 Pandemic in Alberta, Canada
Bibliographic record
Abstract
Background: Our study strived to 1) describe the characteristics of older adults incurring delayed discharge days in Alberta from Apr 01, 2019 to March 31, 2022; 2) examine the prevalence and length of delayed discharge days during the COVID-19 pandemic. Method: We conducted a cross-sectional descriptive study using provincial health administrative data. We included adults ≥65 discharged from hospital from Apr 01, 2019-Mar 31, 2022 in Alberta and whose hospital stay included at least one delayed discharge day. The demographic characteristics of participants were reported in proportions or mean/median. Study period was divided into pandemic waves (pre-pandemic: Apr 1, 2019-Jan 31, 2020; Wave 1: Feb 1, 2020-Aug 31, 2020; Wave 2: Sept 1, 2020-Feb 14, 2021; Wave 3 and beyond: Feb 15, 2021-Mar 31, 2022). Prevalence of delayed discharge in each wave and their median length of stay (IQR) were reported. Results: From Apr 01, 2019 to Mar 31, 2022, there were 367,912 hospitalizations among older adults living in Alberta. 3.73% (n=13,717) contained at least one delayed discharge day. The percentage of delayed discharge prior to COVID-19 and during each wave stayed consistent. Wave 3 had the shortest median length of stay (29, IQR 15-51). Wave 2 (45.2%) and Wave 3 (45.3%) had higher proportion of patients requiring maximal assistance on the Activities of Daily Living (ADLs). From pre-COVID to Wave 3, there were increases in the proportions of patients discharged to long term care (36.4% in pre-COVID to 40.8% by Wave 3). Conclusions: Frequency of delayed discharge hospitalizations was consistent across the pandemic waves. Wave 3 had shorter length of delayed discharge hospitalization. The proportion of patients who were discharged to LTC increased over the course of the pandemic.
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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 | 0.002 | 0.002 |
| 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".