Use of tabletop exercises for healthcare education: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: There is a growing interest in developing interprofessional education (IPE) in the community of healthcare educators. Tabletop exercises (TTX) have been proposed as a mean to cultivate collaborative practice. A TTX simulates an emergent situation in an informal environment. Healthcare professionals need to take charge of this situation as a team through a discussion-based approach. As TTX are gaining in popularity, performing a review about their uses could guide educators and researchers. The aim of this scoping review is to map the uses of TTX in healthcare. METHODS AND ANALYSIS: A search of the literature will be conducted using medical subject heading terms and keywords in PubMed, Medline, EBM Reviews (Evidence-Based Medicine Reviews), CINAHL (Cumulative Index of Nursing and Allied Health Literature), Embase and ERIC (Education Resources Information Center), along with a search of the grey literature. The search will be performed after the publication of this protocol (estimated to be January 1st 2020) and will be repeated 1 month prior to the submission for publication of the final review (estimated to be June 1st 2020). Studies reporting on TTX in healthcare and published in English or French will be included. Two reviewers will screen the articles and extract the data. The quality of the included articles will be assessed by two reviewers. To better map their uses, the varying TTX activities will be classified as performed in the context of disaster health or not, for IPE or not and using a board game or not. Moreover, following the same mapping objective, outcomes of TTX will be reported according to the Kirkpatrick model of outcomes of educational programs. ETHICS AND DISSEMINATION: No institutional review board approval is required for this review. Results will be submitted for publication in a peer-reviewed journal. The findings of this review will inform future efforts to TTX into the training of healthcare professionals.
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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 | 0.104 | 0.088 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.079 | 0.018 |
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".