Understanding how new evidence influences practitioners’ beliefs regarding dry cow therapy: A Bayesian approach using probabilistic elicitation

This study used probabilistic elicitation and a Bayesian framework to quantitatively explore how logically practitioners’ update their clinical beliefs after exposure to new data. The clinical context was the efficacy of antibiotics versus teat sealants for preventing mammary infections during the d...

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Bibliographic Details
Published in:	Preventive veterinary medicine Vol. 139; no. Pt B; pp. 115 - 122
Main Authors:	Higgins, H.M., Mouncey, J., Nanjiani, I., Cook, A.J.C
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 01-04-2017
Subjects:	Animals Anti-Bacterial Agents - therapeutic use Antibiotic Prophylaxis - methods Antibiotic Prophylaxis - veterinary Antimicrobial resistance Attitude Bayes Theorem Bayesian analysis Belief updating Bismuth - administration & dosage Bismuth - therapeutic use Cattle Cephalosporins - administration & dosage Cephalosporins - therapeutic use Clinical priors England Evidence-based veterinary medicine Female Humans Lactation Mastitis, Bovine - drug therapy Mastitis, Bovine - prevention & control Probabilistic elicitation Veterinarians - psychology Clinical priors Belief updating Antimicrobial resistance Evidence-based veterinary medicine Bayesian analysis Probabilistic elicitation
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Summary:	This study used probabilistic elicitation and a Bayesian framework to quantitatively explore how logically practitioners’ update their clinical beliefs after exposure to new data. The clinical context was the efficacy of antibiotics versus teat sealants for preventing mammary infections during the dry period. While most practitioners updated their clinical expectations logically, the majority failed to draw sufficient strength from the new data so that their clinical confidence afterwards was lower than merited. This study provides quantitative insight into how practitioners’ update their beliefs. We discuss some of the psychological issues that may be faced by practitioners when interpreting new data. The results have important implications for evidence-based practice and clinical research in terms of the impact that new data may bring to the clinical community.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0167-5877 1873-1716
DOI:	10.1016/j.prevetmed.2016.08.012