Developing Virtual Gaming Simulations to Promote Interdisciplinary Learning in Addressing Intimate Partner and Gender-Based Violence
Bibliographic record
Abstract
Gender-based violence (GBV) is global issue, requiring specialized knowledge, attitudes, and skills. The most effective responses are interdisciplinary, involving social work, healthcare, and the justice system. While GBV was exacerbated during the pandemic, many students faced a reduced availability of practice opportunities to learn how to respond to GBV. The authors describe a teaching and learning project which expanded access to experiential, interdisciplinary learning across three faculties (social work, nursing, law) by using virtual gaming simulation-based learning (VGSBL). Processes of development and implementation are discussed, along with recommendations for further integration of VGSBL in addressing grand challenges of social work education such as interdisciplinary collaboration in responding to GBV.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".