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Record W2016318568 · doi:10.3138/jvme.35.1.129

Continuing Veterinary Medical Education Needs and Delivery Preferences of Alberta Veterinarians

2008· article· en· W2016318568 on OpenAlexaffvenueabout
Hilary Delver

Bibliographic record

VenueJournal of Veterinary Medical Education · 2008
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMultivariate analysis of varianceAttendanceMedicineWorkloadFocus groupMedical educationVeterinary medicineContinuing medical educationPreferenceMultivariate analysisSpecialtyFamily medicineAnimal welfareContinuing educationMarketingBiology

Abstract

fetched live from OpenAlex

The questionnaire component of a continuing-education needs assessment of Alberta veterinarians is described. A questionnaire about work characteristics, topic priorities, and program delivery preferences was mailed to all licensed Alberta veterinarians, 54% of whom responded. Topic-priority data were factor analyzed, and differences in priorities were examined using multivariate analysis of variance (MANOVA). Seven learning areas emerged: veterinary medicine, professionalism and practice management, animal welfare, livestock practice and epidemiology, prophylaxis and therapeutics, specialty diagnostics, and theriogenology. Associations between delivery preferences and work characteristics were sought using MANOVA and contingency tables analysis. Significant differences were identified in learning-area priorities and preferred program types and times when respondents were grouped by species focus, and there were significant associations between respondents' location and their preferred learning locations. There were no significant associations between location or practice size and preferred program type. Group differences support the need for events tailored by species focus. Delivery preferences suggest that workload and distance are barriers to attendance, although face-to-face events were universally preferred.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.299
GPT teacher head0.492
Teacher spread0.193 · 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

Citations15
Published2008
Admission routes3
Has abstractyes

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