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Record W4280644685 · doi:10.54531/dlvs9567

The use of simulation-based education in cancer care: a scoping review protocol

2022· review· en· W4280644685 on OpenAlexaff
Amina Silva, Jacqueline Galica, Kevin Woo, Amanda Ross‐White, Marian Luctkar‐Flude

Bibliographic record

VenueInternational Journal of Healthcare Simulation · 2022
Typereview
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsQueen's University
Fundersnot available
KeywordsProtocol (science)CancerMedicineComputer scienceAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

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 Unpublished literature will be searched through ResearchGate, OpenGrey and open access theses and dissertations.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quasi-experimental, observational, quantitative and qualitative studies designs, text and opinion papers and unpublished literature.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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.260
GPT teacher head0.594
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2022
Admission routes1
Has abstractyes

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