Experiential Learning for Psychomotor Skills Development of Emergency Medical Services (EMS) Students: An Action Research
Bibliographic record
Abstract
Abstract Background Experiential learning is useful for fostering the development of a wide range of clinical and relational skills. This study aimed to determine the usefulness of experiential learning for psychomotor skills development of emergency medical system (EMS) students. Methods An action research approach was used because it is useful for linking theory with action and practice. FISDAP tool was used to assess and monitor students’ performance and competencies achievement in psychomotor skills. The study was conducted at Prince Sultan College for Emergency Medical Services (PSCEMS), King Saud University (KSU). Skills Performance of 71 EMS students were analysed in the experiential learning implemented throughout the curriculum over 3 years. Results The finding indicates that the students’ skills performance fall below the minimum requirement during the clinical practicum and significantly improved during the time of internship period. The overall success rate across range between 26.58–35.74% across all the psychomotor skills. Whereas during the internship year student’s success rate range between 85.49–99.4% across all the psychomotor skills. Conclusions The findings of this study are promising and show that experiential learning is useful and effective way to develop psychomotor skills and competencies in EMS students. Educators and policy makers can use the findings for strategizing policies for curriculum planning and development.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".