The effects of introducing high-fidelity simulation to preclinical student respiratory therapists.
Bibliographic record
Abstract
INTRODUCTION: This action research study examines the use of high-fidelity simulation (HFS) in a 3-year Respiratory Therapy advanced diploma program offered at a community college located in southwestern Ontario. It seeks to identify if the use of preclinical experiential learning offered through various HFS scenarios had an impact on learners' ability to transition into clinical practicum. The experiential learning theory provided the framework that guided this study as it sought to determine the effect, if any, of HFS on confidence and student anxiety. METHODS: A mixed-method research approach to data collection was used to assess both qualitative and quantitative data. A presimulation, Likert-type questionnaire was completed by 20 participants and utilized to identify learning styles and anxiety with experiential learning activities. The qualitative component of the study involved a focus group exploring four participant's impressions of how HFS affected their ability, anxiety, and competence in preparation for their clinical rotation. Finally, following the focus of action research, the researcher's observations and journaling were used as a method to improve the future delivery and practice of simulation at the researcher's institution. RESULTS: The results of this research project suggest that learners have an increased level of confidence following simulation participation, but that their anxiety levels have not changed when thinking about transitioning into clinical practicum. CONCLUSION: Ongoing research focusing on how this model affects student respiratory therapists' abilities and performance in clinical practicum is needed.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".