Future leaders in a learning health system: Exploring the Health System Impact Fellowship
Bibliographic record
Abstract
The Canadian health system is reeling following the COVID-19 pandemic. Strains have become growing cracks, with long emergency department wait times, shortage of human health resources, and growing dissatisfaction from both clinicians and patients. To address long-needed health system reform in Canada, a modernization of training is required for the next generation health leaders. The Canadian Institutes of Health Research Health System Impact Fellowship (HSIF) is an example of a well-funded and connected training program which prioritizes embedded research and embedding technically trained scholars with health system partners. The program has been successful in the scope and impact of its training outcomes as well as providing health system partners with a pool of connected and capable scholars. Looking forward, integrating aspects of evidence synthesis from both domestic and international sources and adapting a general contractor approach to implementation within the HSIF could help catalyze learning health system reform in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".