Clinical teaching unit design: a realist systematic review protocol of evidence-based practices for clinical education and health service delivery
Bibliographic record
Abstract
INTRODUCTION: The clinical teaching unit (CTU) has emerged as a near-ubiquitous model of clinical education across Canadian and international medical schools since it was first proposed over 50 years ago. However, while healthcare has changed dramatically over this period, the CTU model has remained largely unchanged. We thus aimed to systematically review principles of CTU design that contribute to improved outcomes in clinical education and health service delivery. METHODS AND ANALYSIS: We will perform a realist systematic review in accordance with the Realist And Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) II protocol for realist reviews. Databases, including MEDLINE, Embase, Cochrane Database of Systematic Reviews and Cumulative Index of Nursing and Allied Health Literature (CINAHL), were searched to find primary research articles published from 1993 to 2019 involving CTUs or other teaching wards, and outcomes related to either trainee education or health service delivery. Two reviewers will independently screen studies in a two-stage process. Retrieved titles and/or abstracts of studies will be screened in the first stage, with full texts reviewed in the second stage. Selected articles meeting inclusion criteria will undergo data abstraction using a standardised, pre-piloted form for assessment of study quality and knowledge synthesis. ETHICS AND DISSEMINATION: This review will generate higher quality evidence on the design of CTUs as a model for both clinical education and health service delivery. In addition, further knowledge translation efforts may be necessary to ensure that known best practices in CTU design become common practice.
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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 | 0.233 | 0.257 |
| Meta-epidemiology (narrow) | 0.009 | 0.008 |
| Meta-epidemiology (broad) | 0.028 | 0.014 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.065 | 0.016 |
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".