A systematic review of evidence-based practices for clinical education and health care delivery in the clinical teaching unit
Bibliographic record
Abstract
BACKGROUND: The clinical teaching unit is a widespread clinical training model that requires reform to prepare physicians for practice in the 21st century. In this systematic review, we aimed to identify evidence-based practices in internal medicine clinical teaching units that contribute to improved clinical education and health care delivery. METHODS: We searched several databases from 1993 until Apr. 5, 2021, to identify published studies in inpatient clinical teaching units that involved medical trainees and reported outcomes related to trainee education or health care delivery. We identified emergent themes using a narrative approach and determined confidence in review findings using the Grading of Recommendations Assessment, Development and Evaluation Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) methodology. RESULTS: = 15, 14%) were the most prevalent study designs. Practices identified as contributing to improved clinical education or health care delivery included purposeful rounding (high confidence), bedside rounding (moderate confidence), resource stewardship interventions (high confidence), interprofessional rounds (moderate confidence), geographic wards (moderate confidence), allocating more trainee time to patient care or educational activities (moderate confidence), "drip" continuous models of admission (moderate confidence), limiting duty hours (moderate confidence) and limiting clinical workload (moderate confidence). INTERPRETATION: In this review, we identified several evidence-based practices that may contribute to improved educational and health care outcomes in clinical teaching unit settings. These findings may offer guidance for policies, resource allocation and staffing of teaching hospitals.
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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 | 0.059 | 0.238 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".