Reflections on the Health System Impact Fellowship and the Future of Embedded Research Comment on "Early Career Outcomes of Embedded Research Fellows: An Analysis of the Health System Impact Fellowship Program"
Bibliographic record
Abstract
The Health System Impact (HSI) Fellowship program in Canada offers a transformative approach to health services and policy research (HSPR) training, preparing PhD graduates for diverse career pathways and leadership roles within learning health systems. This commentary builds on Kasaai and colleagues' evaluation of the HSI Fellowship to discuss the diverse career paths of alumni and highlight the multifaceted benefits of the program. Further, we emphasize the need for future research and knowledge mobilization to better understand and evaluate embedded research roles. Developing a robust evaluation framework is essential to capture the unique impacts of embedded research, fostering a culture that prioritizes and integrates it, thereby driving the transformation towards learning health systems.
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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 | 0.068 | 0.174 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.018 | 0.033 |
| 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".