Qualitative analysis of small animal veterinarian–perceived barriers to nutrition communication
Bibliographic record
Abstract
OBJECTIVE: Nutrition is important in preventing and managing disease. Veterinarians are an important source of nutrition information; however, nutrition communication between veterinarians and pet owners is relatively infrequent. The purpose of this study was to conduct a qualitative review of barriers to nutrition communication and possible solutions, reported by small animal veterinarians. SAMPLE: 18 veterinarians from Maryland, Michigan, Virginia, Washington DC, and West Virginia. METHODS: In a qualitative focus group study, 5 virtual focus groups using the Zoom platform were conducted from February 3, 2021, to April 2, 2021. Each focus group was audio recorded, and transcripts were created using Otter.ai software. Transcripts were analyzed in Atlas.ti qualitative data analysis software using a hybrid of inductive and deductive thematic analysis. RESULTS: The 4 barriers to nutrition communication identified by veterinarians were as follows: (1) time, (2) misinformation and information overload, (3) pet owners' apprehension toward new information, and (4) veterinarians' confidence in nutrition knowledge and communication skills. Potential solutions include (1) improving communication and nutrition education, (2) improving and increasing access to client-friendly resources, and (3) empowering credentialed veterinary technicians and support staff to discuss nutrition. CLINICAL RELEVANCE: This study provides guidance for how to focus efforts to break down barriers to nutrition communication in small animal veterinary practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".