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Record W4389327156 · doi:10.3138/jvme-2023-0090

Stress Levels and Stressors of Veterinary Students in Indonesia

2023· article· en· W4389327156 on OpenAlexvenueno aff
Muhammad R. Janjani, Cahyani Fortunitawanli, Adinda R. Fauziah, Bryna Meivitawanli

Bibliographic record

VenueJournal of Veterinary Medical Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStressorGraduation (instrument)Veterinary medicineStress (linguistics)MedicinePsychologyLonelinessMedical educationCurriculumClinical psychologyPsychiatryPedagogy

Abstract

fetched live from OpenAlex

Despite the growing collection of scientific publications on student stress levels, stress experienced by veterinary students in Indonesia has been less investigated. This study assessed the stress levels of veterinary students and investigated the stressors faced by veterinary students in Indonesia, both in undergraduate and professional programs. The study participants were 165 veterinary students from all universities offering veterinary medicine in Indonesia. The Perceived Stress Scale was used to evaluate stress levels, and the modified Veterinary Medical Stressor Inventory was used to indicate several stressors in this study, including academic performance, clinical graduation, negative evaluation, and online classes experienced by veterinary students. The findings show that most veterinary students in Indonesia experienced moderate stress levels. Results also found that female students experienced higher levels of stress than their male counterparts. The multiple regression result shows that stressors belonging to the academic group were the most significant, primarily in undergraduate students. Aside from academics, the rigorous veterinary medicine curriculum, loneliness, and peer competition are the major potential stressors.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.033
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.440
GPT teacher head0.586
Teacher spread0.146 · 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.

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
Published2023
Admission routes1
Has abstractyes

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