Athletic Therapy Students' Perceptions of High-Fidelity Manikin Simulation: A Pilot Study
Bibliographic record
Abstract
Context: Athletic therapy students learn emergency skills through a variety of modes, including students portraying injured athletes and cardiopulmonary resuscitation manikins. Although acceptable and satisfactory forms of teaching, these methods are limited in their ability to create realistic physiological symptoms of injury. Objective: To assess how athletic therapy students perceive their learning needs (LNs) relative to the use of high-fidelity manikin simulation (HFMS) compared with student simulation (SS) in the laboratory setting. Design: Pretest-posttest study design. Setting: Nursing Simulation Centre, Sheridan College, Brampton, Ontario, Canada. Patients or Other Participants: Thirty students from the Bachelor of Applied Health Science (Athletic Therapy) program at Sheridan College in years 2 and 4. Intervention(s): Perceived LNs related to the use of the Laerdal Medical SimMan3G HFMS contrasted with the use of SS for learning to respond to a prescribed emergency scenario. Main Outcome Measure(s): Participants completed questionnaires for both the SS and HFMS environments that consisted of 16 specific LNs spanning the cognitive, psychomotor, and affective domains of learning. Paired t tests and a 2-way analysis of variance were used to analyze the questionnaire data. Results: Participants reported all LNs as being equally important in both environments, but HFMS was identified as a better environment for achieving 13 of the 16 LNs. The mean change from pretesting to posttesting of all LNs in the affective domain improved significantly (P < .05) in the HFMS environment. Year 4 participants deemed HFMS to be a more effective means of learning in the cognitive and psychomotor domains (P < .05). Conclusions: The HFMS experience enhanced athletic therapy students' perceptions of their confidence, base of knowledge, decision-making skills, and overall acute management of critical lifesaving situations. The HMFS environment is a more effective tool for addressing the LNs in the affective domain, which includes skills related to confidence, attitudes, values, and appreciations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".