Interpretive Pedagogy in Action: Design and Delivery of a Violence and Health Workshop for Baccalaureate Nursing Students
Bibliographic record
Abstract
ABSTRACT This article presents the planning and implementation of a Violence and Health Immersion Workshop for undergraduate nursing students. Given the enormous personal and economic costs of violence, the central importance of addressing violence issues in nursing curricula is emphasized. The application of three key interpretive pedagogical strategies is described: choosing critical social science as a conceptual framework for the workshop; placing specific emphasis on the decentering of content; and creating space for learners to explore this difficult issue. Formative and summative evaluations of the workshop indicated that the majority of students found the workshop to be helpful in providing the opportunity to examine and shift their own values, attitudes, and beliefs regarding violence and health. Recommendations for future research include the need for increased knowledge regarding barriers to the implementation of interpretive pedagogies, and for greater insight regarding the process of attending to differences in the participants and the facilitators. AUTHORS Received: February 26, 2004 Accepted: February 22, 2005 Dr. McGibbon is Associate Professor and Ms. McPherson is Assistant Professor, St. Francis Xavier University, School of Nursing, Antigonish, Nova Scotia, Canada. Address correspondence to Elizabeth A. McGibbon, PhD, RN, Associate Professor, Saint Francis Xavier University, School of Nursing, PO Box 5000, Antigonish, Nova Scotia, Canada B2G 2W5; e-mail: emcgibbo@stfx.ca.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".