Career pathways and professional skills of postgraduate students from a dental research‐intensive programme
Bibliographic record
Abstract
BACKGROUND: With current global trends in postgraduate education, graduate programmes must make evidence-based improvements to offer the best programme that aligns with student needs and prepare them for their future career prospects. The aim of this cross-sectional study was to investigate the postgraduation career pathways of MSc and PhD students who graduated within the past 15 years from the McGill University Postgraduate Dental Research Program. MATERIALS AND METHODS: An online questionnaire, composed of 10 closed-ended format items, was used that covered domains such as student profile, career profile, postgraduate skill development, job search experience and satisfaction. Descriptive statistics and interpretative qualitative analysis were used to evaluate student feedback. RESULTS: Sixty-six students responded to the online survey, out of which sixty-two students completed the survey (61% participation rate). The majority of the graduate students, 67% (n = 44), obtained MSc degree in Dental Sciences. Overall, our results showed that most graduates started careers in academia in their original field of study and were satisfied with their income. Most graduates reported "critical and creative thinking" to be the strongest acquired skills during their postgraduate training and identified fierce competition for their position of interest as the main challenge after graduation. DISCUSSION AND CONCLUSION: Our results showed that graduates in dental research appeared to be overall satisfied with their careers after postgraduate research training, both in terms of scope of practice and income. However, strong competition in obtaining the position of their interest seemed to be the main obstacle after graduation.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".