Embedding professional development within the curriculum of graduate programs: An impact survey from biomedical departments in a faculty of medicine
Bibliographic record
Abstract
Although the career pathways of biomedical PhDs are increasingly diverse, graduate education continues to rely upon an apprenticeship model, whereby supervisors train the next generation of researchers. To broaden the core competency skills for research productivity and career development across the Temerty Faculty of Medicine's departments and institutes, a for-credit curricular Graduate Professional Development (GPD) course has been implemented as an integral part of the graduate curriculum in multiple departments. We used three key principles to advance this integrated professional development curriculum: i) a train-the-trainers method for implementation, ii) direct student engagement via a students-as-partners model, one-on-one consultations and videos, and iii) a faculty development program. While programming of this sort has direct benefits, less is known about how students themselves experience curricular GPD. To address the gap in understanding the impact, research was undertaken to inquire about students' perceived skills development and attitudes towards career choices after taking the GPD course. According to our findings, students see the curricular GPD course as a valuable complement to their graduate education and feel more prepared for their next steps in their career exploration. With buy-in from students, faculty and academic leaders, embedded curricular GPD has transformed the academic culture of graduate education in our faculty, helping our students with skills that empower them to be prepared for their own career development to meet the competitive career realities of our time.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".