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Record W2914770685 · doi:10.3138/jvme.0917-134r

Student Experiences in Practice-Based Small Animal Clerkships

2019· article· en· W2914770685 on OpenAlexvenueno aff
Wendy Mandese, Xiaoying Feng, Linda S. Behar‐Horenstein

Bibliographic record

VenueJournal of Veterinary Medical Education · 2019
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationPsychologyMedicineTest (biology)

Abstract

fetched live from OpenAlex

Practice-based clerkships provide a way for students to experience the types of cases, clients, and procedures that they can expect to see in a general practice setting. These clerkships are typically quite different from those offered in teaching hospitals. Forty-seven (65.28%) of the 72 invited veterinary medicine students from three cohorts participated in pre- and post-test surveys designed to compare their expectations to their actual experiences. Students reported significant positive changes in terms of adequate supervision, approachability of practitioner, and comfort level when asking questions, as well as seeing different cases than they see at the teaching hospital. Students reported significant negative changes in terms of their ability to interact with clients as much as they expected with respect to the practice of communication skills, history-taking skills, preventative therapy discussions with clients, and treatment. These findings were supported by written survey comments regarding the most and least helpful portions of the clerkship. We suggest further research to study student experiences over time and to survey practitioners before and following placement with veterinary students; the aim would be to obtain more information about the expectations and success of practice-based clerkships.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.411
GPT teacher head0.574
Teacher spread0.163 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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