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Record W4280514698 · doi:10.3138/jvme-2021-0133

Predictors of Psychological Well-Being among Veterinary Medical Students

2022· article· en· W4280514698 on OpenAlexvenueno aff
McArthur Hafen, Adryanna S. Drake, R.G. Elmore

Bibliographic record

VenueJournal of Veterinary Medical Education · 2022
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStressorPsychological distressPsychologyScale (ratio)MedicineClinical psychologyGerontologyMental healthPsychiatry

Abstract

fetched live from OpenAlex

This study evaluated associations between healthy activities of daily living, common stressors, and psychological well-being among 230 veterinary medical students at Kansas State University. Participants completed the Psychological Wellbeing Scale during the fall semester of 2019. Additionally, students provided information about specific stressors, healthy activities of daily living, and relevant demographic information. Similar to previous studies, participants in this study reported being concerns about heavy workloads, being behind in studies, inefficient study, and academic performance. On average, the students in this study ate fewer than three meals per day, slept less than 7 hours per night, exercised only twice per week, and spent an average of 83 minutes per day on social media platforms. A higher number of daily meals, more days of exercise, and more frequent contact with one's support system, particularly significant others and family members, predicted students' increased psychological well-being. In contrast, lower psychological well-being scores were associated with comparing oneself to others and financial distress. This study identifies potential activities that students can engage in to improve psychological well-being. The discussion section provides specific suggestions for intervention.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch 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 score0.197
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0170.000

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 head0.271
GPT teacher head0.566
Teacher spread0.295 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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