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. RésuméPlusieurs pays reconnaissent les bienfaits de l' apprentissage expérientiel pour améliorer les résultats dans la société et dans les systèmes de santé.Certains organismes subventionnaires ont développé des possibilités de financement afin d' appuyer l' apprentissage expérientiel dans des organismes de santé en dehors des établissements universitaires habituels.Aux États-Unis, l' organisme AcademyHealth a mis au point le Delivery System Science Fellowship (DSSF), qui à son tour a inspiré la création, au Canada, du Programme des bourses d' apprentissage en matière d'impact sur le système de santé (BAIS).Ces deux programmes ont des objectifs similaires : améliorer l' aptitude à la carrière des titulaires de doctorat et accroître la capacité de recherche dans les établissements de santé.Cependant, ces programmes se déroulent dans des systèmes de santé distincts et ont pris des tangentes différentes pour atteindre leurs objectifs.Cet article présente les deux modèles de bourses enchâssées, analyse leurs similitudes et différences, discute des leçons retenues et propose des pistes d' orientation en matière de formation en recherche sur les politiques et les services de santé.T
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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 | 0.033 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".