Simulation use in paediatric student respiratory therapy training.
Bibliographic record
Abstract
At SickKids it is a perennial challenge for clinical leads and preceptors to ensure that student respiratory therapists (SRTs) acquire the entire scope of the paediatric skill set as set out by the 2016 National Competency Profile. As such, simulation has historically been used to supplement the clinical experience for SRTs at Sick Kids; however, the extent to which simulation-based education can replace clinical experience is not clearly established in the literature. At SickKids, we have created an alternative paediatric rotation where the percentage of time spent in the simulation environment is greater than in the traditional, clinical-based rotation. We hypothesize that there should be comparable levels of performance in a simulated setting between students in clinical and simulation-based rotations, as well as comparable measures of self-reported values for cognitive load, self-appraisal, and self-efficacy. Thus far, we have enrolled 59 students, with 54 completing postclinical rotation testing. Follow-up retention testing has been complicated by geographical factors. Following data analysis, we will publish our findings.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".