Practice Patterns in Pediatric Critical Care Medicine
Bibliographic record
Abstract
OBJECTIVE: To obtain current data on practice patterns of the U.S. pediatric critical care medicine workforce. DATA SOURCES: Membership of the American Academy of Pediatrics Section on Critical Care and individuals certified by the American Board of Pediatrics in pediatric critical care medicine. STUDY SELECTION: All active members of the American Academy of Pediatrics Section on Critical Care, and nonduplicative individuals certified by the American Board of Pediatrics in pediatric critical care medicine, were classified as eligible to participate in this electronically administered workforce survey. DATA EXTRACTION: Data were extracted by a doctorate-level research professional. Extracted data included demographic information, work environment, number of hours worked, training, clinical responsibilities, work satisfaction and burnout, and plans to leave the practice of pediatric critical care medicine. DATA SYNTHESIS: Of 1,857 individuals contacted, 923 completed the survey (49.7%). The majority of respondents were white, male, non-Hispanic, university-employed, and taught residents. Respondents who worked full time were on clinical intensive care service for a median of 15 wk/yr and responsible for a median of 13 ICU beds, working a median of 60 hr/wk. Total night call responsibility was a median of 60 nights/yr; about half of respondents indicated night call was in-hospital. Fewer than half were engaged in basic science or clinical research. Compared with earlier data, there was minimal change in work hours and proportion of time devoted to research, but there was an increase in the proportion of female pediatric critical care medicine physicians. CONCLUSIONS: These data provide a description of the typical intensivist and a snapshot of the current pediatric critical care medicine workforce, which may be experiencing a mild-to-moderate undersupply. The results are useful for assessing the current workforce and valuable for future planning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".