Professional development and career-preparedness experiences of STEM Ph.D. students: Gaps and avenues for improvement
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Notice bibliographique
Résumé
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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Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle