Imposter Syndrome in Veterinary Education? How Knowledge and Confidence Affect Treatment of Canine Atopic Dermatitis
Bibliographic record
Abstract
Anecdotal data suggest that clinicians can be confused about the indications, advantages, and disadvantages of treatment options for canine atopic dermatitis (CAD). This may be due to the varying levels of knowledge and confidence among clinicians at different stages of their training and careers. A lack of evidence-based studies of confidence when applying knowledge in veterinary education inspired this research. We surveyed 75 Royal (Dick) School of Veterinary Studies (R(D)SVS) final-year students, 34 general practitioners (GPs), 70 GPs that have undertaken continuing professional development in dermatology, 34 advanced dermatology practitioners (e.g., interns and residents), and 15 dermatology specialists using an online questionnaire with Likert-type scales for each response. Correlations between the levels of education, sources of knowledge about managing CAD, and their understanding of different treatment options were analyzed; p < .001 was deemed significant. The results revealed a significant lack of confidence among students and GPs in treating CAD. In contrast, the groups generally had a similar level of understanding of the management options. The exception to this was a lack of understanding about ciclosporin and antihistamines among students and veterinarians with less dermatology experience. Targets for intervention should therefore aim to improve confidence in clinical application rather than knowledge per se in undergraduate and post-graduate education. Improving confidence in managing CAD will improve the welfare of atopic dogs and their owners.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".