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Record W2013632977 · doi:10.1186/1472-6920-12-17

"What Do They Want Me To Say?" The hidden curriculum at work in the medical school selection process: a qualitative study

2012· article· en· W2013632977 on OpenAlexaff
Jonathan White, Keith Brownell, Jean-François Lemay, Jocelyn Lockyer

Bibliographic record

VenueBMC Medical Education · 2012
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsSelection (genetic algorithm)CurriculumProcess (computing)Medical schoolMedical educationQualitative researchWork (physics)PsychologyPersonnel selectionConceptual frameworkUnintended consequencesPedagogyMedicineSociologyEpistemologyComputer scienceManagementSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: There has been little study of the role of the essay question in selection for medical school. The purpose of this study was to obtain a better understanding of how applicants approached the essay questions used in selection at our medical school in 2007. METHODS: The authors conducted a qualitative analysis of 210 essays written as part of the medical school admissions process, and developed a conceptual framework to describe the relationships, ideas and concepts observed in the data. RESULTS: Findings of this analysis were confirmed in interviews with applicants and assessors. Analysis revealed a tension between "genuine" and "expected" responses that we believe applicants experience when choosing how to answer questions in the admissions process. A theory named "What do they want me to say?" was developed to describe the ways in which applicants modulate their responses to conform to their expectations of the selection process; the elements of this theory were confirmed in interviews with applicants and assessors. CONCLUSIONS: This work suggests the existence of a "hidden curriculum of admissions" and demonstrates that the process of selection has a strong influence on applicant response. This paper suggests ways that selection might be modified to address this effect. Studies such as this can help us to appreciate the unintended consequences of admissions processes and can identify ways to make the selection process more consistent, transparent and fair.

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)0.007
metaresearch head score (Gemma)0.086
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.086
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.039
GPT teacher head0.453
Teacher spread0.414 · 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 designQualitative
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

Citations53
Published2012
Admission routes1
Has abstractyes

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