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Record W2524657204 · doi:10.1080/0142159x.2016.1231915

Opening our eyes to a critical approach to medicine: The humanities in medical education

2016· article· en· W2524657204 on OpenAlexaff
Lester Liao

Bibliographic record

VenueMedical Teacher · 2016
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMentorshipMedical humanitiesSociocultural evolutionPerspective (graphical)Relevance (law)Medical educationIdeologyHuman medicinePsychologyMedicineHumanitiesEngineering ethicsSociologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

This paper examines a recent medical graduate's perspective on how undergraduate education tends to focus on imparting medical knowledge with little reference to the human aspects in clinical medicine. This is problematic because medicine is both about people and practiced by people. Students often have minimal exposure to the humanities prior to and in medical school and are frequently unaware of the societal trends that impact their view of medical practice. Familiarity with the humanities is a crucial means to understanding human nature, recognizing personal sociocultural biases, and practicing patient-centered medicine. This gap in knowledge may be due to the increase in medical information and optimistic ideologies related to medical progress. Philosophical paradigms and historical examples are considered to demonstrate the relevance of both fields in the humanities in understanding the role of moral human agents in applying medical knowledge. Educational changes in the humanities are proposed as a potential solution to our current deficits. Informal changes include mentorship relationships and shifting the general underpinning attitude in medical culture. Formal changes include specific courses teaching a critical approach to medicine. Changes in competency-based education and admissions are also suggested. These amendments are proposed to practice a fuller, truly human medicine.

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)0.003
metaresearch head score (Gemma)0.054
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.642
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.061
GPT teacher head0.406
Teacher spread0.345 · 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 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

Citations16
Published2016
Admission routes1
Has abstractyes

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