Early Career Outcomes of Embedded Research Fellows: An Analysis of the Health System Impact Fellowship Program
Bibliographic record
Abstract
BACKGROUND: This descriptive study reports the early career outcomes of postdoctoral fellows who completed a novel embedded fellowship training program, the Canadian Institutes of Health Research (CIHR) Health System Impact (HSI) Fellowship. The program was designed to support impact-oriented career paths of doctoral graduates, build research capacity within health system organizations, and help to advance learning health systems in Canada. METHODS: Employment of fellowship alumni upon completion of the program were tracked using internet searches of publicly accessible online sources and complemented with program survey data. RESULTS: Descriptive analyses show that all 87 eligible alumni included in the study are currently employed (100% of 87), with 92% employed in Canada. Their employment spans several sectors, including in academic (37%), public (29%), healthcare delivery (17%), and private (14%) sectors. Altogether, 32% of alumni held hybrid roles with an affiliation in academia and another sector. The most common position types were senior scientist (42%), professorships (18%), and director, manager or administrator roles (12%). Program reporting data indicate that these employment outcomes are generally consistent with the group's career aspirations reported at the start of the fellowship program, and that the program receives high ratings from fellows in the extent it is believed to support their career preparedness and readiness (4.49 out of 5). CONCLUSION: We find that HSI Fellow alumni are employed mostly in research-related roles in a range of sectors including, but not limited to academia, that they positively perceive the program's success in elevating their career readiness and potential to make an impact - suggesting that the program may help equip fellows with the skills, readiness and networks for a broad array of employment sectors and roles. The findings are a promising signal of the demand for research talent and the growing capacity for learning health systems in Canada.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".