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Record W4379378393 · doi:10.3138/jvme-2022-0073

Decision-Making and Moral Distress in Veterinary Practice: What Can Be Done to Optimize Welfare Within the Veterinary Profession?

2023· article· en· W4379378393 on OpenAlexvenueno aff
Martin Florián, Lenka Skurková, Lýdia Mesarčová, Monika Slivková, J. Kottferová

Bibliographic record

VenueJournal of Veterinary Medical Education · 2023
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal welfareWelfareEthical decisionVeterinary medicineDistressMental healthMedicineNursingMedical educationPsychologyPolitical sciencePsychiatryLawSocial psychology

Abstract

fetched live from OpenAlex

Veterinarians stand in many contradictory positions, such as moral and ethical representatives of animals and their welfare and the clinic owner, which makes income for them and their families. The article will look at factors in decision making significantly impacting veterinary professionals' mental health. Distress is caused by high societal pressure, as veterinarians must fulfill their profession's requirements. Together with working conditions, it negatively impacts their mental health. The article emphasizes the need for veterinary professionals and future veterinary professionals to have proficiency in animal welfare, animal ethics, and primarily moral decision making. Thus, critical thinking and ethical decision making should be discussed more in the profession and veterinary education.

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)0.017
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.023
Scholarly communication0.0130.009
Open science0.0010.005
Research integrity0.0040.007
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 head0.273
GPT teacher head0.554
Teacher spread0.280 · 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 designQualitative
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

Citations11
Published2023
Admission routes1
Has abstractyes

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