Cross-sectional Survey of Canadian Pediatric Critical Care Transport
Bibliographic record
Abstract
OBJECTIVES: This study aimed to better understand the unique aspects of pediatric critical care transport programs across Canada by characterizing the current workforce of each transport program. METHODS: A cross-sectional questionnaire was sent to the 13 medical directors of Canada's pediatric critical care transport teams, and to 2 nonhospital-affiliated transport services. If a children's hospital did not have a dedicated team for pediatric transport, the regional transport team providing this service was identified. RESULTS: Eight of the 13 pediatric intensive care units surveyed have unit-based pediatric transport teams. The median annual transport volume for the 8 hospital-based teams was 371 (range, 45-2300) with a total of 5686 patients being transported annually. Among patients transported by the 8 teams, 45% (2579 patients) were pediatric patients (older than 28 days and younger than 18 years) and 40% (1022 patients) of the pediatric patients were admitted to the pediatric intensive care units. Eighty-eight percent of the responding teams also transported neonates (older than 28 days), and 38% transported premature infants.A team composition of registered nurse-respiratory therapist-physician was used by 6/13 teams (75%); however, it accounted for only a small proportion of the transports for most of the teams (median, 2%; range, 2%-100%).The average transport time from dispatch (from team home site) to arrival at receiving facility was reported by 6 teams, and has a median of 195 minutes (range, 90-360 minutes). The median distance from home site to the farthest referral site in the catchment area was 700 km (range, 15-2500 km). CONCLUSIONS: This is the first Canadian nationwide study of pediatric critical care transport programs. It revealed a complexity and variability in transport team demographics, transport volume, team composition, and decision-making process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".