Simulation in Social Work: Creativity of Students and Faculty during COVID-19
Bibliographic record
Abstract
Simulation learning plays an important role in social work education, allowing students to explore how theory and practice parameters can be integrated into actual situations they are likely to experience in the field. The arrival of COVID-19 and the sudden cessation of in-field practicum opportunities raised challenges for students to gain needed practice experience. Simulation offers an opportunity to enhance learning in place of some direct experience when that is not available. This paper reports on a simulation development practicum, where students, not able to be in an agency, sought out ways to achieve learning through the development and implementation of simulation learning. This was combined with a literature review. Results showed that student-generated simulation could be used to support direct practice learning. This project also illustrated that social work simulation can be used to help students safely explore areas of practice that they may not be exposed to in practicum through scenarios that cause them to examine how to work with clients where cross-cultural needs exist, and challenge ethical dilemmas in a ‘real-world’ situation while being required to face their biases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.017 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".