Optimism, Self-Esteem, and Social Support as Mediators of the Relationships among Workload, Stress, and Well-Being in Veterinary Students
Bibliographic record
Abstract
Optimism, self-esteem, satisfaction with social support, and coping strategies were examined in relation to study-related subjective workload, stress, and life satisfaction. Questionnaires were distributed to students in the second, third, and fourth years of the five-year BVSc program at Massey University. One hundred fifty usable responses were received. Most respondents were female. Students with more optimism and self-esteem were less stressed than those who were more pessimistic or lower in self-esteem. Students who reported having heavy subjective workloads were more stressed, whereas students with higher self-esteem experienced more well-being. Men and women did not differ on any of the study variables, and there were no differences between students in different years of study. Recommendations include retaining initiatives to build well-being that are already in place. Support resources exist within the university, but students are often reluctant to seek help, so staff need to continue to identify students in need of additional help who might benefit from referral to a health practitioner. Where practicable, support service providers may be able to contribute actively to teaching. As well as helping students build effective social and coping strategies, this approach will provide students with additional information about the services that are available. Students may not see workshops on interpersonal skills or stress management as relevant unless they are linked to their immediate concerns.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".