International Medical Graduates Who Have Been Disciplined: Further Causes and Methods to Improve Quality of Care
Notice bibliographique
Résumé
To the Editor: The article by Dr. Alam and colleagues1 based on a comparison of the rate and nature of offences that received disciplinary action between North American medical graduates (NAMGs) and international medical graduates (IMGs) was of interest to me as an IMG. The authors offered an explanation for causal criteria of disciplinary actions, including discrimination, language barriers, and cultural differences, all of which include certain variability. Other contributing factors not discussed by Dr. Alam and colleagues could include socioeconomic criteria, which could be due to transition periods characterized by no income, no housing, a totally new environment, isolation, and other challenges faced by IMGs,2 which could cause mental stress leading to poor performance and, ultimately, disciplinary action. Additional issues such as heavy workload, which can lead to adverse outcomes, like emotional exhaustion, physical fatigue, and cognitive weariness, affect IMGs and NAMGs alike and can negatively affect quality of care.3 Considering the limitations of the data, the authors were unable to determine when the physicians in their sample began practicing in Canada. Further, there was no discussion of information bias; that is, Alam and colleagues did not explain the main causes of the disciplinary ac tions, nor any actions IMGs took to remedy the situations. It would have been helpful to report location of training, which could have influenced the results. If physicians were trained according to Canadian requirements, that might affect the findings as well. Other studies have described confounding adjustments for disciplinary actions. Newly qualified NAMGs and IMGs alike improve their skills through the organizational culture of training environment along with regular training, as they are skilled in medicine but unable to look after patients’ safety and care.4 The standard of care can get better through different strategies like orientation programs, through which practical challenges are overcome for both IMGs and NAMGs.5 Additionally, mentorship to all graduates through teaching, supervision, guidance, and regular performance assessment allows IMGs to integrate more easily into their new communities,6 which alleviates transitional challenges. Particular courses designed to meet the needs of IMGs with respect to overcoming barriers like language, culture, socialization, and hospital environment can also help.7 Alam and colleagues should have explored these and other potential contributors to their findings. Sadia Hyder, MScResearch student, Memorial University of Newfoundland Faculty of Medicine, St. John’s, Newfoundland, 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 | 0,015 | 0,119 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,005 | 0,001 |
| Intégrité de la recherche | 0,012 | 0,017 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».