Developing Social Work Skills Through Simulation: Exploring Student Engagement with Immigrant Youth
Bibliographic record
Abstract
This paper presents an exploratory mixed methods pilot study examining social work practice with immigrant youth and change in practice skills after engaging in simulation-based learning. Thirteen undergraduate social work students participated in a simulation workshop involving an immigrant female youth accessing community social services. Descriptive statistics were used to understand student’s perceptions of their foundational social work skills-sets post-simulation (case skills, M = 4.21; general skills, M = 3.90; practice competency, M = 3.85; working with adolescents, M = 3.37). There was 100% agreement that the simulation resulted in positive skills development in involving clients in the assessment, interviewing, listening, counseling, cultural sensitivity/humility, observation skills, and cross-cultural practice. Qualitative content analysis explored student’s conceptualization and application of taught concepts, with attention to working with newcomer populations. Findings highlight the positive learning effects of simulation-based education, expanding students understanding of cross-cultural practice and their ability to apply practice skills in complex situations. Participants reflected on their self-locations and commonalities to engage youth. Further research can inform targeted learning objectives for cross-cultural social work education.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".