Exploring the Impact of Toxic Attitudes and a Toxic Environment on the Veterinary Healthcare Team
Bibliographic record
Abstract
The objective of this qualitative study was to compare veterinarians' and Registered Veterinary Technicians' (RVT's) perceptions of the veterinary healthcare team with respect to the impact of toxic attitudes and a toxic environment. Focus group interviews using a semi-structured interview guide and follow up probes were held with four veterinarian groups (23 companion animal veterinarians) and four Registered Veterinary Technician groups (26 RVTs). Thematic analysis of the discussions indicated both veterinarian and RVT participants felt team members with manifestations of toxic attitudes negatively impacted veterinary team function. These manifestations included people being disrespectful, being resistant to change, always wanting to be the "go to person," avoiding conflict, and lacking motivation. When conflict was ignored, or when people with toxic attitudes were not addressed, a toxic environment often resulted. A toxic environment sometimes manifested when "broken communication and tension between staff members" occurred as a result of employees lacking confidence, skills, or knowledge not being managed properly. It also occurred when employees did not feel appreciated, when there was difficulty coping with turnover, and when there were conflicting demands. The presence of people manifesting a toxic attitude was a source of frustration for both veterinarian and RVT participants. Prompt and consistent attention to negative behaviors is recommended to reduce the development of a toxic environment.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".