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Record W4313329505 · doi:10.24911/ijmdc.51-1670618695

Does medical school affect students' empathy? A cross-sectional study measuring empathy levels among Saudi medical students

2022· article· en· W4313329505 on OpenAlexaboutno aff
Rand Alshaya, Milad Alshaya, Shawq Alshowaier, Munira Alsowailem, Abrar Alshamari, Sara Alhuwairini, Alaa Althubaiti

Bibliographic record

VenueInternational Journal of Medicine in Developing Countries · 2022
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyPsychosocialPsychologyPsychological interventionAffect (linguistics)Medical schoolClinical psychologyCross-sectional studyMedicineMedical educationSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Objectives: This research aimed to measure empathy levels among medical students of all academic years and assess whether empathy levels decrease, increase, or remain the same throughout medical school. Also, to measure the mean empathy score and correlated empathy scores to different socio-demographic, personal, and familial variables. Methods: This cross-sectional study included 400 medical students. The study was carried out from October 2020 to May 2021 using online-based surveys, which consisted of questions to collect socio-demographic data and the Toronto Empathy Questionnaire to measure empathy levels. Results: There was no significant change in empathy levels among medical students as they progressed through medical school. However, below-average empathy levels were reported among medical students. There was no significant association between gender or age and empathy levels. Nevertheless, students with a disabled family member had significantly higher empathy levels than others. Conclusion: Effective interventions and measures to enhance empathy and students' psychosocial development are needed to achieve better future clinical outcomes.

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)0.016
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.077
GPT teacher head0.446
Teacher spread0.369 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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