Are Pediatric Critical Care Medicine Fellowships Teaching and Evaluating Communication and Professionalism?*
Bibliographic record
Abstract
OBJECTIVES: To describe the teaching and evaluation modalities used by pediatric critical care medicine training programs in the areas of professionalism and communication. DESIGN: Cross-sectional national survey. SETTING: Pediatric critical care medicine fellowship programs. SUBJECTS: Pediatric critical care medicine program directors. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Survey response rate was 67% of program directors in the United States, representing educators for 73% of current pediatric critical care medicine fellows. Respondents had a median of 4 years experience, with a median of seven fellows and 12 teaching faculty in their program. Faculty role modeling or direct observation with feedback were the most common modalities used to teach communication. However, six of the eight (75%) required elements of communication evaluated were not specifically taught by all programs. Faculty role modeling was the most commonly used technique to teach professionalism in 44% of the content areas evaluated, and didactics was the technique used in 44% of other professionalism content areas. Thirteen of the 16 required elements of professionalism (81%) were not taught by all programs. Evaluations by members of the healthcare team were used for assessment for both competencies. The use of a specific teaching technique was not related to program size, program director experience, or training in medical education. CONCLUSIONS: A wide range of techniques are currently used within pediatric critical care medicine to teach communication and professionalism, but there are a number of required elements that are not specifically taught by fellowship programs. These areas of deficiency represent opportunities for future investigation and improved education in the important competencies of communication and professionalism.
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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 | 0.015 | 0.103 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".