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Record W2415339203

Guidelines for Teaching Cross-Cultural Clinical Ethics

2015· article· en· W2415339203 on OpenAlexaboutno aff
Fern Brunger

Bibliographic record

VenuePubMed Central · 2015
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsnot available
Fundersnot available
KeywordsBioethicsEngineering ethicsPremiseAutonomySociologyHealth careRelativismCompromiseEpistemologySocial sciencePolitical scienceLawPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This paper describes an innovative curriculum in cross-cultural clinical ethics developed by faculty in the Health Ethics and Law program at one Canadian university. The pedagogical approach explicitly uses the notion of relativism in bioethics to encourage (rather than compromise) the use of a standard western bioethics framework for ethical decision-making. It offers an argument, at the intersection of medical anthropology and bioethics, for a pedagogy in ethics that encourages clinicians to challenge Euro-American constructs such as “autonomy” while simultaneously working within a principles-based framework for ethical decision-making. This approach is based on the premise that training in cross-cultural clinical ethics requires a “critical consciousness” approach to cross-cultural health care. Such an approach is enhanced by the use of cases that illustrate how the ideas and practices of medicine itself are culturally embedded and socially, economically, and politically constituted. A pedagogical approach to clinical ethics that works from within a critical consciousness framework encourages a critique of Euro-American based normative assumptions of bioethics while working within a principles-based framework for ethical decision-making. The curriculum features six key learning points taught through case-based discussion. The learning points emphasize culture in its relation to power and underscore the importance of viewing both biomedicine and bioethics as culturally constructed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.083
metaresearch head score (Gemma)0.656
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.574
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0830.656
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.014
Insufficient payload (model declined to judge)0.0000.000

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 head0.658
GPT teacher head0.667
Teacher spread0.009 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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