Life After the HSIF: Lessons From Diffusion of Innovation for Sustaining the Impact of Embeddedness Comment on "Early Career Outcomes of Embedded Research Fellows: An Analysis of the Health System Impact Fellowship Program"
Bibliographic record
Abstract
Kasaai and colleagues examine the career outcomes of alumni from the Canadian Institutes of Health Research's (CIHR's) Health System Impact Fellowship (HSIF), which embeds emerging scholars in health system organizations. The study of the 2017-2019 cohort shows all alumni are employed, with 92% working in Canada, and highlights their presence in academia, public service, healthcare, and private industry. Notably, 37% hold "hybrid" roles, blending academic and other sector work. While the fellowship effectively prepares fellows for impactful careers, the prevalence of hybrid roles raises concerns about sustaining academic partnerships post-fellowship. This commentary explores risks to embedded scholars, such as decentralization, competing innovations, and limited ongoing training, using the Diffusion of Innovations framework. It suggests strategies like strengthening network connectivity, focusing on high-impact innovations, increasing organizational embeddedness, and maintaining adaptability to ensure the long-term success of embedded scholars and the integration of evidence-based innovations in 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 | 0.033 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".