Bibliographic record
Abstract
The stress of veterinary students ranges from the financial stress associated with high student loan debt combined with possible credit card debt, to relational stress due to lack of time to commit to social activities, to uncertainty regarding the ability to perform at the highest level. While this study considers a multifaceted approach to veterinary student stress and ultimate depressive symptoms, the focus is on the financial stress. A common strategy for reducing debt is to increase financial literacy. While this has the potential to help, it is not the sole solution given that students opt into the program for non-financial reasons. A path analysis was used to explore the predictors of financial satisfaction (the inverse of financial stress). The results were then used to predict depression among pre-vet and veterinary students in combination with relationship stress and demographic characteristics. Results indicate that current and expected student loan debt negatively influence financial satisfaction of pre-veterinary and veterinary students. Lower financial and relational satisfaction predict depressive symptoms among students. Among pre-veterinary students, feeling less intelligent than peers and being a sophomore versus a freshman is associated with depressive symptoms. Among current veterinary students, third-year students are more likely to report depressive symptoms than first-year students.
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 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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".