Bibliographic record
Abstract
BACKGROUND: Historically, simulation has mainly been used to teach students hands-on skills in a relatively safe environment. With changes in the patient population, professional regulations and clinical environments, clinical simulation practise (CSP) must assist students to integrate and apply their theoretical knowledge and skills with their critical thinking, clinical judgement, prioritisation, problem solving, decision making, and teamwork skills to provide holistic care and treatment to their patients. CONTEXT: CSP holds great potential to derive a positive transformation in students' transition into the workplace, by associating and consolidating learning from classrooms to clinical settings, and creating bridges between theory and practice. For CSP to be successful in filling the gap, the design and management of the simulation is crucial. INNOVATION: In this article a new framework called 'Clinical simulation practise framework: A knowledge to action strategy in health professional education' is being introduced that aims to assist educators and curriculum developers in designing and managing their simulations. This CSP framework theorises that simulation as an experiential educational tool could improve students' competence, confidence and collaboration in performing professional practice in real settings if the CSP provides the following three dimensions: (1) a safe, positive, reflective and fun simulated learning environment; (2) challenging, but realistic, and integrated simulated scenarios; and (3) interactive, inclusive, interprofessional patient-centred simulated practise.
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 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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".