Strategies for Integrating Cultural Values in Ethics Education for Nurses
Bibliographic record
Abstract
BACKGROUND: Limited usage of cultural values has prevailed in ethics education, probably due to controversial issues of cultural relativism .There is, however, an urgent need to bridge the gap between education and practice to ensure cultural appropriate ethical nursing care.METHODS: A literature review was done to identify transformative theory and strategies that may encourage students to recall, reflect and discuss self-identified morality.RESULTS: The proposed approach draws on transformative learning theory of Mezirow, and introduces strategies for aligning values with ethical theories. The article suggests that instead of imposing moral theories on students, transformative learning strategies should be implemented to reframe students’ ingrained values and realise the assimilation of these values in moral theories.CONCLUSION: Reframing and assimilation are necessary to bridge the gap between ethics education and practice in nursing. The strategies may enable intercultural dialogues that are deemed necessary for harmonious interaction among people with varied and dynamic cultural values and identities.
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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 | 0.042 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| 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".