A provincial assessment of readiness for paediatric emergencies: What are the existing resource gaps in Alberta?
Bibliographic record
Abstract
OBJECTIVES: A large proportion of all emergency visits for paediatric patients across Canada are to general emergency departments (EDs). These centres may not be adequately equipped to provide optimal care for high acuity paediatric emergencies. The objective of this study was to determine paediatric readiness for general EDs and urgent care centres (UCCs) across Alberta and provide each centre with an overall weighted Paediatric Readiness Score (WPRS). METHODS: A paediatric readiness assessment consisting of 55-questions normalized on a 100-point scale was used to survey 107 general EDs, UCCs, and tertiary paediatric EDs in Alberta, Canada. It addresses six primary categories, including Coordination of Patient Care, Physician/Nurse Staffing and Training, Quality Improvement Activities, Patient Safety Initiatives, Policies and Procedures, and Equipment and Supplies. Descriptive statistics were used to present the WPRS score among different groups. Linear regression models were used to explore factors associated with the score. RESULTS: The overall response rate was 59.8%. The median overall WPRS (/100) for all general EDs and UCCs was 48.4 ([interquartile range {IQR}] 17.6). Factors that were correlated with overall score included high paediatric patient volume (24.28, 95% confidence interval [CI]: 10.52 to 38.04) and involvement in a simulation education outreach program (9.38, 95% CI: 1.11 to 17.66). CONCLUSION: Based on this survey, the WPRS of EDs and UCCs across Alberta suggest a need to improve readiness to respond to high acuity paediatric emergencies in these settings.
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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 | 0.003 | 0.006 |
| 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".