Training it Forward: The Role of Embedded Research Fellows in the Network of Scholars Program in Nova Scotia Comment on "Early Career Outcomes of Embedded Research Fellows: An Analysis of the Health System Impact Fellowship Program"
Bibliographic record
Abstract
Kasaai et al describe the career trajectories of embedded scientists trained through the Health System Impact Fellowship (HSIF), showing that 37% of 2017-2019 HSIF alumni continue as embedded researchers in health systems. These findings suggest that the HSIF program effectively supports career readiness in health services and policy research (HSPR). Similarly, the Network of Scholars (NoS) program, launched post-pandemic in Nova Scotia, mirrors these results, with alumni continuing in embedded roles and mentoring a new cohort of learners from undergraduate to postgraduate levels. NoS has incorporated competencies in quality, project management, and innovation to strengthen training for embedded scientists, aligning with the mandate of the Institute of Health Services Policy and Research. Since 2021, NoS has supported over 100 learners, contributing to over 300 rapid reviews and 100 rapid evaluations addressing top health system priorities while enhancing learner competencies and advancing Nova Scotia's Learning Health System (LHS) vision.
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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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".