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Record W2588760078 · doi:10.3138/jvme.0116-009r2

Changes in Affective and Cognitive Empathy among Veterinary Practitioners

2017· article· en· W2588760078 on OpenAlexvenueno aff
Regina Schoenfeld‐Tacher, Jane R. Shaw, Beatrice Meyer-Parsons, Lori R. Kogan

Bibliographic record

VenueJournal of Veterinary Medical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
FundersInternational Business Machines Corporation
KeywordsEmpathyPersonal distressInterpersonal Reactivity IndexPsychologyCognitionPerspective-takingPerspective (graphical)DistressPsychological interventionInterpersonal communicationEmpathic concernClinical psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Clinical empathy is a multi-dimensional concept characterized by four dimensions: (1) affective-the ability to experience patients' or clients' emotions and perspectives, (2) moral-the internal motivation to empathize, (3) cognitive-the intellectual ability to identify and comprehend others' perspective and emotions, and (4) behavioral-the ability to convey understanding of those emotions and perspectives back to the patient or client. The Davis Interpersonal Reactivity Index (IRI) was used to examine the affective and cognitive facets of empathy in veterinary practitioners. The IRI consists of four subscales that measure cognitive (perspective taking and fantasy) and affective (emphatic concern and personal distress) components of empathy. Data from a cross-sectional sample of practicing veterinarians (n=434) were collected. Veterinarians' fantasy scores were lowest for those with the most clinical experience. Personal distress scores were highest among new veterinarians and lowest for those with 26 or more years in practice. High levels of personal distress in the early years of practice are concerning for the professional wellness of veterinarians. To combat this trend, the implementation of resilience-building interventions should be considered to support veterinary practitioners.

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)0.008
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 score0.230
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.323
GPT teacher head0.555
Teacher spread0.232 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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