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Record W3014293810 · doi:10.16965/ijims.2020.105

EMPATHY IN CARIBBEAN MEDICAL STUDENTS ASSESSED USING THE TORONTO EMPATHY QUESTIONNAIRE

2020· article· en· W3014293810 on OpenAlexaboutno aff
Yogesh Acharya, Fernando Isart, Sanket Shah, Pooja Patak, Ahmed Kour, Abida Sayed, Sateesh Babu Arja

Bibliographic record

VenueInternational Journal of Integrative Medical Sciences · 2020
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyPsychologyClinical psychologyMedical educationApplied psychologySocial psychologyMedicine

Abstract

fetched live from OpenAlex

Introduction: Empathy is the emotional process to understand a patient's state of being and current emotion.Empathy, through humanization of medical students, plays an important role while learning and practicing the art of medicine.Our study aims to quantify empathy as an indicator of humanization, in medical students throughout their education.Subjects and Methods: A cross-sectional questionnaire survey was performed on basic medical and clinical science students at Avalon University School of Medicine, Curacao.Standard Toronto Empathy Questionnaire [TEQ] was utilized to quantify the empathy.Results: Average TEQ scores of the basic students in MD1 -MD4 were 51.55 ± 4.16, 49.42±3.58,46.72±4.60,48.86±6.17respectively.Overall TEQ scores were slightly higher in basic science students in comparison to the clinical students (48.82 ± 5.12 vs 48.74 ± 4.01, P=0.46).Conclusions: Empathy scores were higher in basic science medical students in comparison to the clinical students.Lack of progression of empathy amongst medical students needs to be addressed.We recommend medical schools to adapt and instill the virtue of empathy in the course curriculum.

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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.072
GPT teacher head0.460
Teacher spread0.389 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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