Exploring the Effectiveness of Virtual Simulation-Based Learning in Enhancing Clinical Skills in Social Work Education
Bibliographic record
Abstract
Simulation-based learning (SBL) effectively teaches competency-based skills in social work education, allowing students to practice, reflect, and process their knowledge to improve practice. This study examined the use of SBL to support clinical practice development for both live (online) and virtual gaming simulation (VGS) experiences to enhance responses to childhood experiences of intimate partner violence (CEIPV). 68 students were recruited from a Bachelor of Social Work (BSW) course between 2020 and 2024. Participants engaged in either two live (online) simulated client scenarios (SCS) (n = 52) or two live (online) SCS with an additional VGS experience (n = 16) containing the same scenarios with opportunities to practice different responses and observe best practices. Data in the form of reflective practice and observationally coded evaluations of clinical competency performance was gathered before and after the course. Students significantly improved their skills when practicing with SCS, and preliminary evidence suggests better outcomes when students practiced the scenario through VGS. Using both live and virtual SBL is a promising approach to enhancing social work competence. Implications for social work education will be discussed.
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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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".