Identification of a partnership model between a university, for-profit, and not-for-profit organization to address health professions education and health inequality gaps through simulation-based education: A scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Healthcare providers in rural and remote (R&R) areas of Canada do not have the same access to skills development and maintenance opportunities as those in urban areas. Simulation-based education (SBE) is an optimal technique to allow healthcare providers to develop and maintain skills. However, SBE is currently limited mainly to universities or hospital-based research laboratories in urban areas. The purpose of this scoping review is to identify a model, or components of a model, that outline how a university research laboratory can collaborate with a for profit and not-for-profit organization to facilitate the diffusion of SBE into R&R healthcare provider training. METHODS AND ANALYSIS: This scoping review will be guided by the methodological framework introduced by Arksey and O'Malley in 2005 and the Methodology for Joanna Briggs Institute Scoping Reviews. Ovid MEDLINE, PsycINFO, Scopus, Web of Science, and CINAHL will be searched for relevant articles published between 2000 and 2022, in addition to grey literature databases and manual reference list searches. Articles describing a partnership model or framework between academic institutions and non-profit organizations with a simulation or technology component will be included. Titles and abstracts will be screened, followed by a full-text screening of articles. Two reviewers will participate in the screening and data extraction process for quality assurance. Data will be extracted, charted, and summarized descriptively to report key findings on potential partnership models. CONCLUSION: This scoping review will provide an understanding on the extent of existing literature regarding the diffusion of simulators for healthcare provider training through a multi-institutional partnership. This scoping review will benefit R&R parts of Canada by identifying gaps in knowledge and determining a process to deliver simulators to train healthcare providers. Findings from this scoping review will be submitted for publication in a scientific journal.
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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 | 0.136 | 0.115 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.041 | 0.009 |
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".