Embedded Research Fellows: Charting New Paths for Impact; Comment on "Early Career Outcomes of Embedded Research Fellows: An Analysis of the Health System Impact Fellowship Program"
Bibliographic record
Abstract
With more PhDs working outside of academia, embedded research programs are emerging as one way to broaden the skills of students and bridge the gap between theory and practice. Limited data has been collected on the impact of these programs. The paper by Kasaai et al provides a glimpse into the early career paths of alumni from Canadian Institutes of Health Research's (CIHR's) Health Systems Impact (HSI) Fellowship. The results suggest demand for embedded researchers is high and their career prospects are promising. Beyond that, the paper raises several issues that warrant further attention. First is the evolution towards learning health systems (LHSs) and the role embedded researchers might play in this. Second is the potential of embedded researchers to span the worlds of academia and practice. Third is how to measure impact in non-academic research roles. This commentary explores these issues and suggests ways that embedded researcher programs can contribute to each.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.049 | 0.050 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".