Impact of a Competency Training Course on the Job Profile of Veterinary Medicine Graduates in Northeastern Brazil
Bibliographic record
Abstract
The present study identified professional training aspects in veterinary medicine at a federal public university in the Northeast of Brazil based on the graduates perception. The outcome of this study is anticipated to facilitate updating of pedagogical project execution of the courses in related areas to improve their curriculum. Hence, the course coordination started monitoring the graduates using an online questionnaire on the course webpage. The form consisted of questions about the graduate's sociodemographic information; academic training; professional performance; evaluation about the humanistic and technical skills; expertise and knowledge areas; job market perception; and curriculum evaluation. This survey was performed following the ethical criteria. Data were analyzed using the Chi-square test ( p < .05). Graduates work mainly in the Northeast region of the country, and are employed less than a year after graduation. Moreover, they are working in their training area with a compatible salary. They recognized the generalist profile of their training. They also believed that the course promotes articulation between teaching, and research and extension, as well as consider the discipline contents more theoretical than practical. Upon completing the course, 41% of the graduates felt prepared for the job market, especially in the area of animal health. The results obtained allowed us to understand the socioeconomic, demographic, and professional profiles of the trained professionals. Therefore, monitoring the trajectory of graduates can support decisions about the didactic-pedagogical adjustments aimed at promoting the quality of professional training, thereby meeting the job market demands.
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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 | 0.003 |
| 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) | 0.003 | 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".