Training for Health System Improvement: Emerging Lessons from Canadian and US Approaches to Embedded Fellowships
Bibliographic record
Abstract
The benefits of supporting experiential learning for improved health and societal outcomes have been recognized in many countries. A number of funding organizations have developed competitive funding opportunities to support experiential learning in health system organizations outside of the traditional university setting. AcademyHealth in the US is an early innovator that pioneered the Delivery System Science Fellowship (DSSF) and inspired Canada's creation of the Health System Impact (HSI) Fellowship program. The DSSF and HSI Fellowship have similar objectives: to improve the career readiness of doctorally prepared graduates and to build research capacity within health system organizations. However, the programs have taken different approaches to achieve these objectives and operate in different healthcare systems. This paper outlines the two models of embedded fellowships, analyzes their commonalities and differences, discusses lessons learned and suggests future directions for health services and policy research training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".