Learning strategies among adult CHD fellows
Bibliographic record
Abstract
OBJECTIVE: Subspecialisation is increasingly a fundamental part of the contemporary practice of medicine. However, little is known about how medical trainees learn in the modern era, and particularly in growing and relatively new subspecialties, such as adult CHD. The purpose of this study was to assess institutional-led and self-directed learning strategies of adult CHD fellows. METHODS: This international, cross-sectional online survey was conducted by the International Society for Adult Congenital Heart Disease and consisted primarily of categorical questions and Likert rating scales. All current or recent (i.e., those within 2 years of training) fellows who reported training in adult CHD (within adult/paediatric cardiology training or within subspecialty fellowships) were eligible. RESULTS: A total of 75 fellows participated in the survey: mean age: 34 ± 5; 35 (47%) female. Most adult CHD subspecialty fellows considered case-based teaching (58%) as "very helpful", while topic-based teaching was considered "helpful" (67%); p = 0.003 (favouring case-based). When facing a non-urgent clinical dilemma, fellows reported that they were more likely to search for information online (58%) than consult a faculty member (29%) or textbook (3%). Many (69%) fellows use their smartphones at least once daily to search for information during regular clinical work. CONCLUSIONS: Fellows receiving adult CHD training reported a preference for case-based learning and frequent use of online material and smartphones. These findings may be incorporated into the design and enhancement of fellowships and development of online training resources.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".