Pediatric Endocrinology Education Among Trainees: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Pediatric endocrinology education is a fundamental part of all pediatric endocrinology training. Hence, understanding the current methods used for training learners on skills required and understanding methods or topics that may be underexplored could help improve the quality of training. OBJECTIVE: This study aims to explore training and assessment strategies used in pediatric endocrinology training across medical education programs through a scoping review. METHODS: Search strategy was developed with a librarian, and bibliographic databases (e.g., MEDLINE and EMBASE) were searched from January 2005 to July 2024. Pilot screenings ensured consistent inclusion/exclusion decisions among reviewers. Full-text articles were included if they were related to pediatric endocrinology education and focused on medical learners. RESULTS: We included 45 of 5814 sources of evidence for data extraction. Majority focused on knowledge of Type 1 and Type 2 diabetes and diabetes ketoacidosis (N = 18), followed by differences in sex development and pubertal assessment (N = 12). The most frequently used training method was through didactics. Additionally, the most frequently used assessment measures included knowledge tests (N = 25). Also, a limited number of studies targeted obesity (N = 2), gender care (N = 3), thyroid (N = 1) and hypoglycaemia (N = 1), and no studies targeted common topics such as bone health and adrenal insufficiency. CONCLUSION: This review reveals the current emphasis on diabetes-related topics and traditional teaching in pediatric endocrinology education. It suggests a need for more innovative methods, like simulation-based learning and varied assessment techniques, to better equip trainees. Addressing these gaps can improve trainee confidence, patient care, and health outcomes for children with endocrine disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.020 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".