Empowering trainee fellows today will ensure that we build tomorrow’s leaders in gastroenterology
Notice bibliographique
Résumé
Dear Editor, We read with great interest the article by Gardezi et al. exploring the experiences of gastroenterology (GI) fellows across Saudi Arabia.[1] The authors are to be commended for undertaking one of the first national, multiregional surveys of fellowship training, highlighting both progress and persisting gaps in education and mentorship. This work provides valuable insights into a rapidly evolving training landscape. The authors employed a cross-sectional online survey distributed nationwide. The questionnaire was thoughtfully developed and pilot-tested, with responses analyzed using descriptive statistics and thematic analysis. Such methodology offers an efficient way to capture fellows’ perceptions, though reliance on self-reported data introduces subjectivity and recall bias. Importantly, the sample size (n = 54) represents a reasonable proportion of this subspecialty cohort but may not fully reflect the heterogeneity of all training centers, particularly with no representation from the northern region, similar to previous limitations in assessment of Inflammatory bowel disease training.[2] Fellows reported overall moderate to high satisfaction (mean 3.96/5), with strengths including diverse case exposure, structured rotations, and strong endoscopic training. However, significant deficiencies were noted in mentorship (2.78/5), access to advanced therapeutic endoscopy, structured teaching sessions (40.7%), and leadership or research training (≈50%). These findings mirror concerns raised in international surveys, such as variability in advanced endoscopy exposure in the UK[3] and Canada.[4] This study represents the first multiregional survey of GI fellows in Saudi Arabia, capturing both quantitative and qualitative data. The mixed-methods design provides a nuanced picture of training strengths and challenges. By aligning results with the Saudi Commission for Health Specialties (SCFHS) competency framework, the study highlights important areas for national program alignment. As the authors acknowledge, self-perception may not correlate with objective measures of competence. Cross-sectional design precludes assessment of longitudinal training outcomes. Further, anonymity—while ethically sound—prevents center-specific benchmarking that could guide targeted interventions. Finally, the absence of objective procedural logs, simulation exposure data, or faculty perspectives limits triangulation of findings. The findings underscore the urgent need for standardized curricula, structured mentorship frameworks, and equitable access to simulation-based endoscopy training. International models, such as the British Society of Gastroenterology’s national mentorship initiatives and the American Society for Gastrointestinal Endoscopy’s fellows’ programs, could provide adaptable templates. Moreover, embedding leadership and research as mandatory fellowship components would better prepare graduates for academic and administrative roles. We encourage future research to incorporate: Longitudinal assessment of fellows’ outcomes post-graduation (e.g., independent practice readiness, academic productivity). Faculty perspectives to balance trainee-reported data. Objective procedural and competency metrics, potentially integrated into the SCFHS e-portal for real-time monitoring. National benchmarking to ensure consistency across low- and high-volume centers. Gardezi et al. provide a timely contribution to the discourse on gastroenterology training reform in Saudi Arabia. Their findings resonate with global challenges in GI education and should prompt stakeholders to prioritize structured mentorship, simulation, and leadership training. By addressing these gaps, the Kingdom can ensure the next generation of gastroenterologists is both globally competent and locally responsive to healthcare needs. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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 | 0,007 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,007 | 0,008 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,011 | 0,019 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,004 |
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 ».