The Perceived Effectiveness of Various Forms of Feedback on the Acquisition of Technical Skills by Advanced Learners in Simulation-Based Health Professions Education
Bibliographic record
Abstract
Simulation-based health professions education (SBHPE) is a valuable approach for healthcare professionals to develop and refine technical skills in a safe environment. Feedback plays a crucial role in the acquisition of these skills, but little research has explored the effectiveness of augmented (knowledge of results (KR) and knowledge of performance (KP) versus intrinsic feedback types for advanced learners. Therefore, this study aimed to determine what type of feedback is perceived to be most effective by advanced learners when acquiring complex technical skills in SBHPE. The study followed the test and evaluated phases of the design-based research (DBR) framework. A total of 23 advanced care paramedics (ACPs) participated in the study and received feedback in the form of KR, KP, and intrinsic feedback while using the intraosseous (IO) access simulator. Participants completed a survey to evaluate their learning experience and rank the perceived effectiveness of each feedback type. The results of this study indicated that KP was perceived as the most effective type of feedback and KR was perceived as the least effective feedback, with intrinsic feedback being in the middle. This work provides insights into the use of augmented and intrinsic feedback for advanced learners in an SBHPE environment, but future work to assess the actual learning effects of these types of feedback 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 | 0.010 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".