Guidelines for Using Simulations in Qualitative Research on Social Work Practice Competencies
Bibliographic record
Abstract
The use of simulation (i.e., trained actors) has gained much attention in social work as a method of teaching, learning, and student assessment. Simulation has also been used in medicine as a research method in studying practice competencies. The use of simulation as a part of research design is relatively new in social work. Particularly, little is known about how simulations can be combined with well-established qualitative research methods. We posit that simulation can further advance qualitative research on social work practice, which requires a highly complex set of skills that are procedural, cognitive, affective, and relational. Drawing from two study examples, we propose guidelines for how simulations can be incorporated in qualitative research on complex practice competencies essential for enhancing the quality of health and social services.
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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 | 0.446 | 0.596 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.011 | 0.013 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.021 | 0.016 |
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".