CIHR Health System Impact Fellows: Reflections on "Driving Change" Within the Health System
Bibliographic record
Abstract
Learning health systems necessitate interdependence between health and academic sectors and are critical to address the present and future needs of our health systems. This concept is being supported through the new Canadian Institutes of Health Research (CIHR) Health System Impact (HSI) Fellowship, through which postdoctoral fellows are situated within a health system-related organization to help propel evidence-informed organizational transformation and change. A voluntary working group of fellows from the inaugural cohort representing diversity in geography, host setting and personal background, collectively organized a panel at the 2018 Canadian Association for Health Services and Policy Research Conference with the purpose of describing this shared scholarship experience. Here, we present a summary of this panel reflecting on our experiential learning in a practice environment and its ability for impact.
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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 | 0.073 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.048 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.011 | 0.036 |
| 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".