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Record W7162765040 · doi:10.67010/1028-2424.2037

Choosing Who to Be: Teaching Ethics Through Value Conflicts and Identity Reflection

2025· article· en· W7162765040 on OpenAlexafffund
Jennifer Kwon, HsingChi von Bergmann

Bibliographic record

VenueJournal of Medical Education · 2025
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsVancouver Coastal HealthUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsIdentity (music)Value (mathematics)NegotiationReflection (computer programming)Professional ethicsProfessional developmentNarrativeReflective practiceExperiential learning

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.013
Scholarly communication0.0110.008
Open science0.0020.010
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.059
GPT teacher head0.514
Teacher spread0.455 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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