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Record W2766580084 · doi:10.3168/jds.2017-13217

Comparison of online, hands-on, and a combined approach for teaching cautery disbudding technique to dairy producers

2017· article· en· W2766580084 on OpenAlexaff
Charlotte B. Winder, S.J. LeBlanc, Derek B. Haley, K. Lissemore, Michael A. Godkin, T.F. Duffield

Bibliographic record

VenueJournal of Dairy Science · 2017
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
Fundersnot available
KeywordsLogistic regressionNonsteroidalMedicineBlock (permutation group theory)AnesthesiaPsychologyMathematics

Abstract

fetched live from OpenAlex

The use of pain control for disbudding and dehorning is important from both an animal and industry perspective. Best practices include the use of local anesthetic, commonly given as a cornual nerve block (CNB), and a nonsteroidal anti-inflammatory drug. The proportion is decreasing, but many dairy producers do not use local anesthesia, perhaps in part due to lack of knowledge of the CNB technique. Although this skill is typically learned in person from a veterinarian, alternative methods may be useful. The objective of this trial was to determine if there were differences in the efficacy of online training (n = 23), hands-on training (n = 20), and a combined approach (n = 23) for teaching producers to successfully administer a CNB and disbud a calf. The primary outcome was block efficacy, defined as a lack of established pain behaviors during iron application. Secondary outcomes were background knowledge (assessed by a written quiz), CNB and disbudding technique (evaluated by rubric scoring), time taken, and self-confidence before and after evaluation. Associations between training group and outcome were assessed with logistic regression, ordered logistic regression, and Cox-proportional hazard models, with a random effect for workshop. Block efficacy was not different between training groups, with 91% successful in both combined and online groups, and 75% in the hands-on trained group. Online learners had poorer technical scores than hands-on trainees. The combined group was not different from hands-on. Time to block completion tended to be longer for the online group (62 ± 11 s), whereas time to disbudding completion was not different between hands-on (41 ± 5 s) or combined trainees (41 ± 5 s). The combined group had the highest pre-evaluation confidence score, and remained higher after evaluation than online but was not different than hands-on. Although we saw some statistical differences between groups, absolute differences were small and block efficacy was similar. This suggests online training can be a useful tool for motivated producers who lack access to hands-on training.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.294
GPT teacher head0.544
Teacher spread0.249 · 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 designNon-randomized trial
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

Citations19
Published2017
Admission routes1
Has abstractyes

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