Brazilian Dental Students’ Intentions and Motivations Towards Their Professional Career
Bibliographic record
Abstract
Investigating career motivations and intentions of dental students provides a better understanding of their role in society and contributes to the debate on dental education and practices. This study describes the profile, career choice motivations, and career intentions of Brazilian dental students and evaluates factors related to these choices. A cross-sectional study was carried out among dental students from three Brazilian public universities (N=915), with a response rate of 83.7 percent. Students (N=766) responded to a self-administered questionnaire about sociodemographic factors, reasons for choosing dentistry as a career, and future career intentions. Job conception was found to be the main reason for choosing dentistry as a profession. Most students intended to become specialists and work in both the public and private sectors simultaneously. Female students (OR 2.23, 95 percent CI=1.62-3.08), low-income students (OR 1.86, 95 percent CI=1.10-3.13), and students beginning their program (OR 1.87, 95 percent CI=1.22-2.85) were more likely to work in the public and private sectors simultaneously than other types of students. This study suggests that choice of career and career plans are influenced by factors related to the students' characteristics and their conception of the profession. The opportunity to combine private and public dental practice may be viewed as a way to achieve income and job security.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".