Using a Positive Psychology Lens to Understand How Veterinary Medicine Learning Contexts Promote Student Thriving and Inhibit Frustration
Bibliographic record
Abstract
To develop a veterinary workforce equipped for long-term professional success, educational institutions must prioritize their students’ well-being. Most approaches focus on building assets within the individual, like stress management, to limit negative outcomes, like burnout. Our research proposes a positive psychology-based model of student thriving that instead emphasizes the pervasive role of the social climate within a context. Basic Psychological Needs Theory (BPNT) posits that social relationships at the institutional, faculty and staff, and peer levels will promote student thriving and limit frustration through the satisfaction or frustration of the three psychological needs of competence, autonomy, and relatedness. Veterinary medical students across the United States ( N = 202) completed a survey, and we used structural equation modeling to test how their institution's social climate predicted positive student outcomes (i.e., hope and life satisfaction) and a negative outcome (i.e., burnout) mediated by psychological need satisfaction and frustration. Students’ perceptions of positive aspects of their institution's social climate ubiquitously predicted each variable in the model. Overall, the model positively predicted psychological need satisfaction ( R 2 = .44), hope ( R 2 = .67), and life satisfaction ( R 2 = .51), and negatively predicted psychological need frustration ( R 2 = .34) and burnout ( R 2 = .87). Findings emphasize the role veterinary medicine peers, faculty, and staff play in creating learning environments that support student thriving while limiting their frustration. By leveraging the interpersonal qualities posited by BPNT's parent theory, self-determination theory, veterinary medical colleges can build a culture of student support that benefits all within their system.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".