Advisory workgroup recommendations on the use of clinical simulation in respiratory therapy education.
Bibliographic record
Abstract
Clinical simulation has become established as a commonly used educational approach in respiratory therapy, though questions remain with regards to the evidence basis for its use in some contexts. In conjunction with the development of a new iteration of the National Competency Framework (NCF), the National Alliance of Respiratory Therapy Regulatory Bodies (NARTRB) reaffirmed its desire to continue to recognize the use of simulation as an educational tool. Given the expressed uncertainty as to best practices in the use of clinical simulation in entry-to-practice respiratory therapy education programs, the NARTRB requested the creation of an expert workgroup to develop a list of recommendations from which an implementation plan could be developed for the next iteration of the NCF. The resulting advisory workgroup recommendations are intended to inform the application of simulation in education programs relative to the attainment of entry-to-practice competencies as outlined in the current National Competency Profile. The recommendations presented focus on the use of clinical simulation for formative and summative assessment of respiratory therapy competencies. The recommendations indicate that the use of formative assessment in clinical simulations along with deliberate practice has been clearly shown to improve learning outcomes for which the simulations are designed. However, it is advised that the use of clinical simulation for the summative assessment of competency (e.g., to assess readiness for practice) be exercised cautiously in the context of respiratory therapy education. A number of requisite instructional design factors that should be considered before implementing summative simulation-based assessments are identified, including the validation of summative assessment tools.
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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 | 0.113 | 0.210 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.015 | 0.006 |
| Research integrity | 0.066 | 0.031 |
| Insufficient payload (model declined to judge) | 0.049 | 0.053 |
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".