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Everyday classism in medical school: experiencing marginality and resistance

2005· article· en· W2118135589 on OpenAlexaffabout
Brenda L. Beagan

Bibliographic record

VenueMedical Education · 2005
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAlienationSocial classPsychologyCohortResistance (ecology)Social isolationIsolation (microbiology)Medical schoolClass (philosophy)Affect (linguistics)Working classSocial exclusionEveryday lifeSocial psychologyMedical educationClinical psychologyMedicinePsychiatryPolitical science

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore the medical school experiences of students who self-identify as coming from a working-class or impoverished family background. METHODS: A questionnaire was administered to Year 3 medical students at a Canadian medical school and in-depth interviews were held with 25 of these students (cohort 1). The same methods were repeated with another Year 3 class 3 years later (cohort 2). RESULTS: While having (or not having) money was the most obvious impact of social class differences, students also discussed more subtle signs of class that made it easier or more difficult to fit in at medical school. Students from working-class or impoverished backgrounds were significantly less likely to report that they fitted in well, and more likely to report that their class background had a negative impact in school. They were also more likely to indicate awareness that a patient's social class may affect their health care treatment. CONCLUSION: Students from working-class or impoverished backgrounds may experience alienation in medical school. Through the commonplace interactions of 'everyday classism' they may experience marginalisation, isolation, disrespect and unintentional slights. At the same time, they suggest that their experiences of exclusion may strengthen their clinical practice.

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)0.001
metaresearch head score (Gemma)0.043
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: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.043
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)0.0380.000

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 head0.016
GPT teacher head0.372
Teacher spread0.356 · 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

Citations97
Published2005
Admission routes2
Has abstractyes

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