The Emerging Health Leaders network experience: Reflections and lessons learned from a grassroots movement
Bibliographic record
Abstract
The Emerging Health Leaders (EHL) network was established in 2006 to enhance the leadership capacity of early careerists in the health sector in Canada. Ten years later, the development of the next generation of health leaders continues to be a focus for system leaders. Despite the rhetoric, financial investments in leadership development remain stagnant. This article describes the network's experience in supporting the professional development needs of aspiring leaders across Canada. Successes and challenges regarding the development of the network are discussed, as are the results from a recent benchmarking survey, which identify remaining gaps and priorities for aspiring young leaders. Recommendations are also provided-based on the EHL experience-about how senior leaders can look to support and leverage the contributions of young leaders.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".