Fellowship training: a qualitative study of scope and purpose across one department of medicine
Bibliographic record
Abstract
BACKGROUND: Fellowship training follows certification in a primary specialty or subspecialty and focusses on distinct and advanced clinical and/or academic skills. This phase of medical education is growing in prevalence, but has been an "invisible phase of postgraduate training" lacking standards for education and accreditation, as well as funding. We aimed to explore fellowship programs and examine the reasons to host and participate in fellowship training, seeking to inform the future development of fellowship education. METHODS: During the 2013-14 academic year, we conducted interviews and focus groups to examine the current status of fellowship training from the perspectives of division heads, fellowship directors and current fellows at the Department of Medicine, University of Ottawa, Canada. Descriptive statistics were used to depict the prevailing status of fellowship training. A process of data reduction, data analysis and conclusions/verifications was performed to analyse the quantitative data. RESULTS: We interviewed 16 division heads (94%), 15 fellowship directors (63%) and 8 fellows (21%). We identified three distinct types of fellowships. Individualized fellowships focus on the career goals of the trainee and/or the recruitment goals of the division. Clinical fellowships focus on the attainment of clinical expertise over and above the competencies of residency. Research fellowships focus on research productivity. Participants identified a variety of reasons to offer fellowships: improve academic productivity; improve clinical productivity; share/develop enhanced clinical expertise; recruit future faculty members/attain an academic position; enhance the reputation of the division/department/trainee; and enhance the scholarly environment. CONCLUSIONS: Fellowships serve a variety of purposes which benefit both individual trainees as well as the academic enterprise. Fellowships can be categorized within a distinct taxonomy: individualized; clinical; and research. Each type of fellowship may serve a variety of purposes, and each may need distinct support and resources. Further research is needed to catalogue the operational requirements for hosting and undertaking fellowship training, and establish recommendations for educational and administrative policy and processes in this new phase of postgraduate education.
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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 | 0.026 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".