Pediatric critical care capacity in Canada: a national cross-sectional study
Bibliographic record
Abstract
Abstract Background Pediatric intensive care unit (PICU) capacity is a current and future health system challenge in Canada. Despite experiencing two pandemics over the last 15 years and surges in PICU admissions every winter, the bed capacity of Canadian PICUs and their ability to accommodate surges in demand are unknown. Methods We conducted an internet-based cross-sectional survey to gather information from Canadian PICUs regarding PICU characteristics, medical staffing, therapies provided, and anticipated challenges related to surge management. The survey was completed by a representative of each PICU and validated by PICU Directors. Quantitative survey results were summarized as counts, proportions, and ratios while qualitative response was analyzed using inductive content analysis. Results Representatives from all 19 PICUs located in 17 hospitals completed the survey and reported having 275 ( 217 level 3 & 58 level 2) funded beds with 298 physical bed spaces. Two PICUs representing 47 beds ( 35 Level 3 & 12 Level 2) are specialized cardiac ICUs. Roughly 13385, 13419, 11430 and 12315 Canadian children were admitted to these PICUs in the years 2018, 2019, 2020 & 2021, respectively. During a surge, PICUs reported being able to add 5.9 ± 3.4 (range: 0 – 14) beds per unit and a total of 108 temporary surge beds. Several barriers for the successful implementation of surge plans were identified. Interpretation Canadian pediatric critical care capacity is comparable to other high-income countries, though our ability to respond to a pandemic/epidemic surge with significant pediatric critical illness may be limited.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".