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Record W4403100771 · doi:10.1080/10401334.2024.2409695

Two-Dimensional Deaths? A Discourse Analysis of Patient Death in Preclinical Tutorial Cases at a Canadian Medical School

2024· article· en· W4403100771 on OpenAlexafffundabout
Paula Cameron, Victoria Luong, Olga Kits, Wendy A. Stewart, Sarah Burm, Stephen G. Miller, Simon Field, Anna MacLeod

Bibliographic record

VenueTeaching and Learning in Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsNova Scotia Health AuthorityDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMedicineMedical educationPsychologyFamily medicine

Abstract

fetched live from OpenAlex

Introduction: The prospect of death is everywhere, but seldom directly addressed, in undergraduate medical education (UGME). Despite calls for UGME curricula to address the complex social and emotional aspects of death and dying, most curricula focus on biomedical, legal, and logistical aspects, or concentrate these topics within palliative care content and/or in simulations with simulated patients and manikins. We aimed to add to death education scholarship by exploring the complexities of death and dying within two dimensional simulations—i.e., in the text-based cases used in Case-Informed-Learning (CIL). Method: We conducted a critical discourse analysis exploring how death and dying were discursively constructed in the formal, planned curriculum at one medical school. We used two methods: (1) Document Analysis: We developed a template to analyze 127 cases regarding their discursive constructions of death and dying; (2) Longitudinal Interviewing: We conducted semi-structured interviews with a cohort of 12 medical students, twice annually throughout their medical program (total 92 interviews). We collectively analyzed data, attuning to how the format, content, and purpose of each case discursively constructed death and dying. Results: There were 127 tutorial cases included in the undergraduate, pre-clerkship case-informed curriculum. In the five (4%) cases featuring a patient who dies, death and dying were discursively constructed as: (1) predictable; (2) a plot device; (3) a cautionary tale; (4) an epilogue; (5) deliberate and careful; and (6) an absence. Very few cases highlighted death and dying in their titles, learning objectives, or questions, and where it did feature, it was framed a biomedical fact or outcome. Only one case allowed for a nuanced, in-depth and open-ended discussion of patient death and dying, but it was scheduled at a time that prevented meaningful engagement. This glossing over the complexities of death was identified as a missed opportunity by students, who, as their clinical placements loomed, were eager to broach this topic in detail with tutors and other teaching faculty. Discussion: Death was often a conspicuous absence in this CIL curriculum. In the few cases that featured the death of the main patient character, multiple discourses were mobilized that worked together to construct death as something that happens elsewhere, outside the parameters of core curriculum. In other words, death happens—predictably, slowly, as a means to an end and the result of moral failures, in the case or somewhere in the future—but was not the primary concern. To deepen engagement with these subjects in CIL, we encourage medical educators to attend to representations of patient death by considering the format, content, purpose, and timing of these cases. Conclusion: Carefully rendered cases thoughtfully embedded in the curriculum offer tremendous potential. We suggest nuanced cases featuring patient death, with plenty of space and time for discussion, reflection, and storytelling may help address gaps in formal UGME preclinical curricula addressing death and dying.

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)0.005
metaresearch head score (Gemma)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.403
Teacher spread0.378 · 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 teacher head, not a consensus.

Study designObservational
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
Published2024
Admission routes3
Has abstractyes

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