Choosing Who to Be: Teaching Ethics Through Value Conflicts and Identity Reflection
Bibliographic record
Abstract
Background: This study introduces a reflective learning approach that combines ethical dilemmas with axiological analysis to explore how students' value hierarchies shape their professional identity formation. Moving beyond traditional frameworks such as Principlism, this approach invites learners to examine the tensions between personal and professional values, encouraging critical reflection on how their decisions relate to multiple identities and evolving professional commitments. Methods: Two open-ended ethical dilemmas were distributed via an anonymous online survey using Qualtrics. This method was intentionally chosen to elicit narrative responses from a large cohort of students while providing space for candid reflection on complex, value-laden situations. Responses were analyzed using axiological analysis in NVivo (Version 14, QSR International), focusing on identifying and interpreting conflicting values in students' written reflections. Results: The findings show that both scenarios deepened engagement with ethical dilemmas by prompting students to examine and prioritize competing values. Scenario 1 revealed tensions between professional obligations to patients and personal or family commitments, while Scenario 2 highlighted conflicts involving legal and institutional requirements, patient-centred care, and professional integrity. Students demonstrated value-based reasoning by negotiating these tensions through contextual judgment and effective communication, supporting self-reflection and professional identity development beyond rule-based decision-making. Conclusion: This study expands traditional case-based ethics learning by offering a structured, scalable method for engaging learners in value-oriented reflection. It highlights the utility of combining ethical scenarios with axiological analysis to enhance understanding of professional challenges while offering educators insight into how learners' values shape identity formation.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".