Pediatric critical care capacity in Canada
Bibliographic record
Abstract
Objectives: Pediatric intensive care unit (PICU) capacity is a current and future health system challenge. Despite experiencing two pandemics in as many decades and surges every winter, we have little to no information on PICU capacity in Canada. Our objective was to characterize the bed capacity of Canadian PICUs and their ability to accommodate surges in demand. Methods: We conducted a cross-sectional survey to gather information from Canadian PICUs regarding funded/physical beds, unit characteristics, medical staffing, therapies provided, and challenges related to surge management. The survey was completed by a representative from each PICU and validated by PICU Directors. Quantitative survey results were summarized as counts and proportions, while the free-text response was summarized using inductive content analysis. Results: Representatives from all 19 Canadian PICUs located in 17 hospitals completed the survey and reported having 275 (217 level 3 and 58 level 2) funded beds and 298 physical bed spaces. Of these, 47 beds (35 level 3 and 12 level 2) are in two specialized cardiac PICUs. Roughly 13,385, 13,419, 11,430, and 12,315 children were admitted in the years 2018, 2019, 2020, and 2021, respectively. During a surge, PICUs reported being able to add 5.9 ± 3.4 beds per unit totaling up to 108 temporary surge beds. Several barriers for the successful implementation of surge plans were identified. Conclusions: Canadian pediatric critical care capacity is comparable to that in many other high-income countries, though our ability to respond to a pandemic/epidemic with significant pediatric critical illness may be limited.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".