Adult Experiences with Hospitalization in Alberta, Canada During the COVID-19 Pandemic: A Comparative Cross-Sectional Study
Bibliographic record
Abstract
Little is known about the experiences of those hospitalized during the COVID-19 pandemic in Canada. Our aims were to (a) report on the experiences, (b) compare with historical results, and (c) assess for potential monthly differences of patients hospitalized in the early months of the COVID-19 across Alberta. A random sample of adults was surveyed within 6 weeks of discharge from 93 hospitals, using a modified version of the Canadian Patient Experiences Survey - Inpatient Care (CPES-IC). Discharges from April to September 2020 comprised the "during COVID-19 pandemic" cohort, while April to September 2019 formed the historical one. Results were reported as percent in "top box", indicative of the most positive answer choice. Odds of reporting a "top box" response were calculated while controlling for demographic and clinical features. In total, 23,412 surveys (11,344 during COVID-19, 12,068 historical) were obtained. Those hospitalized during COVID-19 had higher odds of "top box" ratings on 17 of 39 questions examined, and lower odds on 2 questions (information about the admission process, inclusion of family/friends in care decisions). The remaining 20 questions showed no difference between the 2 cohorts. Our results indicate that respondents hospitalized during the early months of the pandemic had experiences that were largely better or comparable to pre-pandemic. This speaks to the dedication and resilience of staff who provided care during challenging circumstances. Our findings may assist in mitigating fears among those who are hesitant to seek medical care during future pandemics or public health emergencies, including subsequent waves of COVID-19.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".