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Record W4405612743 · doi:10.3138/jvme-2024-0034

Veterinary Student Skills Learned at an Access to Care Clinic: Beyond Medicine and Surgery

2024· article· en· W4405612743 on OpenAlexvenueno aff
Elizabeth E. Alvarez, Kelly P. Schultz, Simon Lygo‐Baker, Ruthanne Chun

Bibliographic record

VenueJournal of Veterinary Medical Education · 2024
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumSocioeconomic statusMedicineEthnic groupFamily medicinePsychologyMedical educationPopulationPedagogy

Abstract

fetched live from OpenAlex

Incorporating curriculum to effectively help veterinary students learn how to provide accessible quality care to all pet owners is needed. The primary aims of this study are to explore how a 2-week rotation at a veterinary medical service-learning clinic (Wisconsin Companion Animal Resources, Education, and Social Services [WisCARES]) improves (a) comfort in working with clients from diverse race and low socioeconomic (SES) backgrounds and (2) confidence in leading cases, communication skills, and providing a spectrum of care options. Students were surveyed at five time points pre-rotation: mid-week 1, mid-week 2, end of rotation, and 1 month post. A total of 115 survey series were at least partially completed. Of the 97 responses that included background information, 68 (70%) students reported having “no to a few weeks” of experience working with diverse or low SES populations. When comparing themselves to before starting the rotation, student responses indicated increased comfort (mean = 4.54, standard deviation [ SD] = 0.54) and compassion (mean = 4.42, SD = 0.78) working with low-income or homeless populations, more comfort interacting with members of different race or ethnicity groups (mean = 4.21, SD = 0.82), and more appreciation for the human–animal bond (mean = 4.42, median = 5). Students also reported that spending time at WisCARES positively impacted their confidence in a clinical setting, managing and communicating about financial decisions, and approaching cases creatively. Giving students an opportunity to lead cases with clients from diverse races and low SES backgrounds can enhance levels of comfort with practice and improve confidence.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.520
GPT teacher head0.642
Teacher spread0.122 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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