Awareness of Veterinary Social Work in Veterinary Professionals
Bibliographic record
Abstract
Although the specialty field of veterinary social work (VSW) is becoming more prominent within social work education and training programs, there is a lack of awareness of what a VSW professional can do when embedded within the veterinary medicine profession. Social workers often interact with individuals who have companion animals and with veterinary medicine professionals. However, there is a lack of collaborative care, evident in the empirical research and practice experience, between social work and veterinary medicine. Understanding the veterinary profession's awareness of VSW is essential to the development of interdisciplinary collaborations. VSW is a specialized practice of social work that includes four explicit components: 1) grief and loss of pets; 2) compassion fatigue and well-being of veterinarian professionals; 3) animal-assisted interventions; and 4) the link between animal abuse and interpersonal violence. A researcher-created survey was distributed to veterinary practice affiliates connected with a college of veterinary medicine ( N = 100). The aims of this anonymous Qualtrics survey were to: 1) explore veterinary professionals’ understanding of the concepts of VSW; 2) identify which resources, needs, and support veterinary medicine professionals need for the four components of VSW; and 3) determine if veterinary professionals desire a social worker in their settings to collaborate. The results demonstrated a lack of awareness of concepts by veterinary professionals of VSW. Respondents also expressed a desire for increased social work presence in their veterinary practice. This study highlights the importance of incorporating VSW into veterinary medical training to increase interprofessional collaborations and interventions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".