Procedural Competence Among Faculty in Academic Health Centers: Challenges and Future Directions
Bibliographic record
Abstract
Increasingly, faculty are taking on more direct responsibilities in patient care because of reductions in resident work hours, increasing admissions, and an endless push for efficiency. Furthermore, the rise of different career tracks in academia (i.e., patient care, research, education, or administration) and a drive for efficiency and subspecialization have placed additional strains on academic health centers. Combined, these factors have led to faculty increasingly being placed in the position of supervising bedside procedures that they may have not performed in years or with tools they have never trained with at all. Despite these challenges, procedural retraining for faculty remains nonstandardized across most academic health centers. The resulting lack of procedural competence among faculty creates a number of challenges for the different parties involved.In this Perspective, the authors discuss the nature of the current problem of faculty procedural competence and the challenges it poses for faculty and academic health centers, medicolegal ramifications, and the challenges it poses to the faculty-trainee relationship. The authors then suggest several strategies to delineate and resolve this problem. To delineate the problem, they suggest single-center surveys to address the current paucity of data. To resolve the problem, they suggest the consideration of some modest, low-cost interventions such as having backup systems in place for procedure supervision (e.g., procedural service teams or interventional radiologists) and providing faculty with opportunities to retrain.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".