Identification of a partnership model between a university and not-for-profit organization to address health professions education and health inequality gaps through simulation-based education: A scoping review
Bibliographic record
Abstract
Simulation-based education is a key aspect of health professions education used to aid healthcare providers in developing and maintaining clinical skills. Rural and remote healthcare providers have limited access to skills development opportunities. Training tools such as simulators are primarily limited to university and hospital-based research centers in urban areas. This scoping review aimed to examine current literature to identify a partnership model involving academic institutions and non-profit organizations (NPOs) that focuses on facilitating the wider distribution of simulators. The five-stage Arksey and O'Malley methodological framework for conducting scoping reviews and the Joanna Briggs Institute Manual for Evidence Synthesis was used to guide the scoping review. The search was conducted on five literature databases, three grey literature databases and through manual reference searching with an applied time frame of 2000 to 2022. The search identified 15 articles that met the eligibility criteria and were included in the study. Analysis of the articles revealed that no partnership model currently exists that facilitates the production and distribution of simulators through a partnership between academic institutions and NPOs. Establishing the partnership, acquiring funding, implementation, monitoring and evaluation, and dissemination were identified as key stages of a multi-institutional partnership. Further research is necessary to fill the gaps of the partnership process pertaining to the development and production of simulators to train healthcare providers.
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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 | 0.046 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".