Exploring the Factors that Affect the Happiness of South African Veterinarians
Bibliographic record
Abstract
Happiness is a new field of study in various fields, including health care and veterinary science. Workplace-related happiness, or subjective well-being in the work environment, has become a prominent research field. The happiness of veterinarians has gained academic interest globally over recent years. Previous research indicated that increased happiness levels of employees have social, personal and possible financial gain for employers and employees. The objectives of this study were to determine the factors that affect the happiness of South African veterinarians and develop a conceptual model based on the identified factors. A cross-sectional study using a quantitative survey was conducted using a standardized questionnaire. Of 2,182 registered veterinarians, 360 practicing veterinarians completed the survey and the results were statistically analyzed using exploratory factor analysis. The results indicated that the factors influence in the workplace, social relationships, satisfaction with work-life balance, purpose, optimism, work satisfaction, work stress, and leisure were identified as having significant statistical relationships with the happiness of veterinarians. Managerial recommendations are provided based on the research findings. This study is the first known study to examine the factors that affect the happiness levels of veterinarians. The study forms the base for similar research to be conducted in other countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".