The Use of Simulation as a Component of Clinical Education in Health Professional Entry to Practice Programmes
Bibliographic record
Abstract
Purpose: Finding sufficient physiotherapy clinical placement opportunities to meet clinical education requirements has been an ongoing challenge for Canadian physiotherapy programmes. Simulation may offer viable alternatives to traditional models. The objective of the scoping review is to describe the current use and design of simulation as a component of clinical education to develop competencies in health professional programmes. Method: This scoping review followed the JBI scoping review methodology. Five databases were searched, MEDLINE, CINHAL, EMBASE, ERIC, and SportDiscus, using variants of the search terms health professions education, simulation, and competency. Independent reviewers applied inclusion criteria in two stages: the abstract and title screen and the full-text review. Data was charted and analysed according to objectives. Results: Thirty studies were included in the review. There was large variability in the implementation of simulation, including level of learner, length of the simulation, competency, and simulation design. Most studies ( n = 25) evaluated the inclusion of simulation within clinical education or compared simulation to traditional clinical education experiences. Seven studies compared different simulation designs to replace clinical education time. Conclusions: The variety of simulation experiences described and being implemented provides programmes with the flexibility to design simulation according to needs and resources. Rigorous research is recommended to contribute to an understanding of the most effective simulation design.
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 | 0.038 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".