Exploring Social Justice Through Art in a Community Health Nursing Course
Bibliographic record
Abstract
Social justice and health equity are foundational to community health nursing. Arts-based pedagogy has learning and reflexive value for community and population health education within nursing and health professions curricula have been increasingly used in health care and in promoting health, including in nursing education. However, research has not explored the use of arts to teach community health nursing students about social justice. The objective of this study was to understand how the inclusion of a collaborative artistic process relates to the understanding of social justice issues for second-year baccalaureate nursing students enrolled in a community health nursing course. Visual art and symbolic components were added to an existing group concept mapping assignment of community health nursing interventions from a social justice approach. We engaged in analysis within interpretive phenomenological inquiry to understand and share students’ experiences with constructing and giving meaning to symbols and art pieces, internalizing the concept of social justice, and collaborating with group members. Students used symbols and visual representation to explore social justice and health. Students’ narrative reflections encompassed experiences finding personal power, engaging in empathy, reflecting on their own position and privilege, and benefitting from non-traditional forms of learning. Students recounted group processes that deepened their understanding of concepts, increased their appreciation of the need for advocacy, and enabled creative freedom in the context of collective vision. The addition of a collaborative creative, artistic process enhanced students’ learning about social justice and health.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".