Shaping Social Justice Values Through Inclusive Assessment and Debriefing of eLearning Modules
Bibliographic record
Abstract
Background: Nurses need to recognize how intersectionality shapes the experiences of individuals and families navigating complex health systems. Guided reflection on complex social justice issues serves as an approach to move beyond simply understanding social determinants of health toward shaping core professional values of developing nurses to promote lasting change. Method: Third-year Canadian undergraduate prelicensure nursing students co-created assignment expectations, completed online modules, and submitted initial reflections before class in a mandatory social justice course. In-class debriefing was based on students' reflections and cofacilitated by subject matter experts. Students completed a final reflection that focused on advocating for social change. Results: Student feedback, reflections, and grades as well as faculty observations support the success of this interactive student-centered approach. Conclusion: A flexible approach to debriefing modular content informed by universal design for learning and simulation theory enables nurse educators to promote in-depth, meaningful, and lasting student learning. [ J Nurs Educ . 2024;63(1):48–52.]
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".