Professional development and career-preparedness experiences of STEM Ph.D. students: Gaps and avenues for improvement
Bibliographic record
Abstract
This study presents the experiences of current science, technology, engineering and mathematics (STEM) Ph.D. students and alumni with respect to professional development opportunities in their Ph.D. training. Specifically, it investigates if and how the Ph.D. training supports graduates to pursue non-academic and non-R&D roles, which have become increasingly common career paths post-graduation. A mixed-methods questionnaire was developed to obtain quantitative and qualitative data regarding the graduate school experiences of current Ph.D. students and recent Ph.D. graduates pursuing diverse career paths. The study investigates the values, needs, and conceptions of professional development from the student perspective, as well as the contributions of peers and mentors in graduate school towards their professional development. Experiences of Ph.D. alumni are used to identify the barriers for transitioning to the first job post-graduation and to provide an assessment of the current professional development opportunities in Ph.D. programs. It is reported that although Ph.D. training allowed alumni to develop a robust skillset that includes research, teaching, and scientific writing; some common barriers associated with obtaining a job post-graduation were lack of awareness about career options, limited or no professional networks outside academia, and a lack of preparation and support for non-academic job transitions. Through analyzing the student perspective on various aspects of professional development, the study identifies gaps and avenues for improvement for professional development in Ph.D. training, including increased awareness of diverse career paths for STEM PhDs, increased networking opportunities for PhD students with sectors outside academia, embedding professional development in the PhD curriculum, and others; so that programs can support students in entering the labor market in a variety of careers that extend beyond academia and traditional R&D jobs, using interventions that resonate with the students and meet their needs.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".