Understanding the motivations, deterrents, and incentives for rural Albertan veterinary practice
Bibliographic record
Abstract
Introduction: The shortage of rural veterinarians is a growing concern globally. This shortage increases the risk of significant negative impacts on livestock management, agriculture, and public health in rural and remote communities. To provide concrete solutions to sustain our rural veterinarian workforce, we examine motivations, incentives, and deterrents to rural veterinary practice (RVP). We do this through a qualitative study in Alberta, Canada, which is a geographically unique and understudied context. Methods: We surveyed veterinary students and practicing veterinarians, obtaining 124 responses. Data were analyzed using thematic analysis. Results: Results revealed key motivating factors that influence attraction and retention included personal and family considerations that require living in rural contexts, the nature of strong relationships that develop in rural communities, experiencing a range in work factors that enhances professional development, feeling fulfilled by rural veterinary work, and exposure during veterinary school leading to a strong interest in rural settings. Deterrents included limited resources and supports in rural contexts, personal and family needs that require living in urban settings, and challenges inherent to rural communities and environmental characteristics. Finally, key incentives included better salary and benefits, financial incentives, tuition/debt forgiveness, enhanced mentorship, fewer on-call duties, and tailored incentives. Discussion: Strong alignment between student and practicing veterinarian motivations, deterrents, and incentives was observed, extending previous findings that only look at the perceptions of a single group. The results corroborated previous findings, while revealing that the same motivations and deterrents remained important for students and PVs in Alberta's geographically unique context. Finally, they provided key insights to inform policy, practice, and education developments to enhance attraction and retention rates of rural veterinarians, contributing to a path forward for addressing the rural shortage of veterinary services.
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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 | 0.004 | 0.009 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".