Perceptions of 24/7 In-Hospital Intensivist Coverage on Pediatric Housestaff Education
Bibliographic record
Abstract
BACKGROUND: In recent years, the focus on patient safety and housestaff supervision has led to a steady increase in institutions providing 24/7 in-hospital (also known as in-house, henceforth referred to as IH) coverage by pediatric intensivists. Effects of this increased attending physician presence on education of pediatric housestaff have not been studied. We hypothesized that IH coverage would decrease perceived autonomy of housestaff and negatively affect their preparedness to be independent attending physicians on completion of training. METHODS: A secure, anonymous, Web-based survey was sent to pediatric intensivists in the United States and Canada, and pediatric critical care fellows and pediatric residents at academic centers across the United States. Questions focused on perceptions of IH coverage and housestaff educational experience. RESULTS: We report 1323 responses from 147 institutions (center response rate 74%). Although 96% of respondents stated that the PICU provides "a good educational experience," only 50% of pediatric intensivists and 67% of housestaff feel that housestaff are prepared for independent practice after training in an IH model. Compared with those training in home-call models, respondents currently working in IH models have more favorable perceptions of the effects of IH coverage on housestaff autonomy (P < .0001), supervision (P < .0001), and preparation for independent practice (P < .0001). CONCLUSIONS: Pediatric intensivists and housestaff express concern regarding the preparation of housestaff training in a 24/7 IH attending model. An important priority for institutions using or considering a 24/7 IH attending coverage model is the balance between adequate housestaff supervision and autonomy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".