The use of simulation-based education in cancer care: a scoping review protocol
Bibliographic record
Abstract
Abstract Background Simulation-based education can be an effective strategy to educate nurses and physicians across the continuum of cancer care. However, there is still a lack of studies collating and synthesizing the literature around the types, functionalities and delivery systems of simulation-based education to educate different professional groups about cancer care. Aim To collate and synthesize the literature on how simulation has been used to educate nurses and physicians about cancer care. Methods Scoping review methodology according to the Joanna Briggs Institute framework. Published literature is going to be searched through Medline (OVID), CINAHL, EMBASE and PsycINFO. Unpublished literature will be searched through ResearchGate, OpenGrey and open access theses and dissertations. Articles will be considered if the population is nurses (including nurse practitioners) and/or physicians, if they use any type of simulation as an educational strategy as the concept of interest, and if the context is cancer care. This review will consider experimental, quasi-experimental, observational, quantitative and qualitative studies designs, text and opinion papers and unpublished literature. Expected results Results from this scoping review will generate a solid underpinning for nursing and medical community to empower evidenced innovation through the further development of simulation-based educational interventions.
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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 | 0.099 | 0.085 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.065 | 0.012 |
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".