The power of affective learning strategies on social justice development in nursing education
Bibliographic record
Abstract
Nursing professional values are critical for the practice of nurses, yet the development of curricula fundamentally supporting these values has been slow to develop. The question remains as to the best teaching strategies that foster the integration of these core values as a key focus for nurses throughout their professional practice. The purpose of this article is to report the findings of a research project related to an affective learning strategy, and the potential of such strategies to guide undergraduate nursing students in the development of professionalism. While conducting a study related to the use of poverty simulation and the attitudes of nursing students, participants provided compelling narratives highlighting a greater understanding of the constructs of social justice; a potentially more profound purpose for this pedagogical strategy. Focus group narratives revealed themes focusing on the concepts of professional nursing values, specifically social justice. The themes included: The American Dream Isn’t for Everyone, Trapped in my Own Life, Completely out of Control, and It’s Just Not Enough. Findings showed that participants experienced grave realizations regarding not only the experience of poverty, but of the widespread social norms that contribute to injustices for a vast population in our society. This research contributes to the body of literature regarding the use of affective learning strategies as an effective way to teach nursing professional values, such as social justice, to enhance nursing graduates’ ability to integrate these values in their own practice.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".