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Record W2474461653 · doi:10.1111/medu.12995

Experiences of medical students who are first in family to attend university

2016· article· en· W2474461653 on OpenAlexaff
Caragh Brosnan, Erica Southgate, Sue Outram, Heidi Lempp, Sarah Wright, Troy R. Saxby, Gillian Harris, Anna Bennett, Brian Kelly

Bibliographic record

VenueMedical Education · 2016
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsToronto East General HospitalToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsCultural capitalAmbivalenceSocial capitalContext (archaeology)DisadvantageMedical educationMedical schoolEquity (law)PsychologySociologyMedicineSocial psychologySocial sciencePolitical science

Abstract

fetched live from OpenAlex

CONTEXT: Students from backgrounds of low socio-economic status (SES) or who are first in family to attend university (FiF) are under-represented in medicine. Research has focused on these students' pre-admission perceptions of medicine, rather than on their lived experience as medical students. Such research is necessary to monitor and understand the potential perpetuation of disadvantage within medical schools. OBJECTIVES: This study drew on the theory of Bourdieu to explore FiF students' experiences at one Australian medical school, aiming to identify any barriers faced and inform strategies for equity. METHODS: Twenty-two FiF students were interviewed about their backgrounds, expectations and experiences of medical school. Interviews were recorded, transcribed and analysed thematically. Findings illustrate the influence and interaction of Bourdieu's principal forms of capital (social, economic and cultural) in FiF students' experiences. RESULTS: The absence of health professionals within participants' networks (social capital) was experienced as a barrier to connecting with fellow students and accessing placements. Financial concerns were common among interviewees who juggled paid work with study and worried about expenses associated with the medical programme. Finally, participants' 'medical student' status provided access to new forms of cultural capital, a transition that was received with some ambivalence by participants themselves and their existing social networks. CONCLUSIONS: This study revealed the gaps between the forms of capital valued in medical education and those accessible to FiF students. Admitting more students from diverse backgrounds is only one part of the solution; widening participation strategies need to address challenges for FiF students during medical school and should enable students to retain, rather than subdue, their existing, diverse forms of social and cultural capital. Embracing the diversity sought in admissions is likely to benefit student learning, as well as the communities graduates will serve. Change must ideally go beyond medical programmes to address medical culture itself.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0040.003
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.373
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations108
Published2016
Admission routes1
Has abstractyes

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