Notice bibliographique
Résumé
To the Editor: While burnout in medical students, residents, and early-career physicians has been recently investigated,1,2 a significant and distinct subgroup of trainees has been overlooked in clinician burnout research: MD/PhD trainees. There are important factors that differentiate this group from other clinical trainees, and as such, existing research may not reflect burnout rates amongst MD/PhD trainees. Specifically, there are potential risk factors and protective factors that merit being considered for future research. MD/PhD trainees have longer training durations than their MD-only counterparts, which has been estimated to amount to an average financial loss of $2.3 million (Canadian dollars) per trainee.3 This estimate was based on debt and potential years of earnings lost, but since MD/PhD trainees are generally older, there may be additional financial pressures that go along with life changes such as getting married, purchasing a home, or starting a family. Many MD/PhD trainees looking to establish a career in academia may also feel the added stress of preparing for two careers in a limited time frame. Unfortunately, the existing curriculum in medical school and residency offers limited time for research endeavors, making academic-track positions difficult to attain. On the other hand, MD/PhD training may protect against burnout from stress associated with residency matching, as MD/PhD trainees may have had more time to determine their desired medical specialty, and may have had opportunities to interact and form long-term relationships with clinicians in their desired specialty. In addition, they may have publication records and other accolades that enhance their odds of matching to their desired specialty. It is unclear what role social support may play in burnout among MD/PhD trainees. It is possible that MD/PhD trainees spend their free time on research and have less time to seek social support from friends, family, or resources at school, or it may be that they have had more time to develop social support networks over the course of their training. MD/PhD trainees may also be more likely to be married, which has been previously shown to be a protective factor against burnout.1 Ultimately, the lack of data on MD/PhD trainees leaves us to wonder about the net effect on burnout of these potential risk factors and protective factors. Future research should seek to investigate these questions and determine the nature of the stressors that MD/PhD trainees experience. This will inform the implementation of strategies within MD/PhD training programs to mitigate potential risks. Isabelle A. VallerandMD/PhD student, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; [email protected]
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».