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Record W2236484017 · doi:10.3352/jeehp.2015.12.52

Pre-clinical versus clinical medical students’ attitudes towards the poor in the United States

2015· article· en· W2236484017 on OpenAlexfundno aff
Danial Jilani, Ashley K. Fernandes, Nicole J. Borges

Bibliographic record

VenueJournal of Educational Evaluation for Health Professions · 2015
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
FundersUniversity of Windsor
KeywordsMedical educationPsychologyMathematics educationMedicine

Abstract

fetched live from OpenAlex

This study assessed the poverty-related attitudes of pre-clinical medical students (first and second years) versus clinical medical students (third and fourth years). First through fourth year medical students voluntarily completed the Attitude Towards Poverty scale. First and second year students were classified together in the preclinical group and third and fourth year students together in the clinical group. A total of 297 students participated (67% response rate). Statistically significant differences were noted between pre-clinical and clinical students for scores on the subscales personal deficiency (P<0.001), stigma (P=0.023), and for total scores (P=0.016). Scores across these subscales and for total scores were all higher in the clinical group. The only subscale which did not show statistical significance between pre-clinical and clinical students was the structural perspective. Medical students in their clinical training have a less favorable attitude towards the poor than their preclinical counterparts.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.512
GPT teacher head0.674
Teacher spread0.162 · 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 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

Citations3
Published2015
Admission routes1
Has abstractyes

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