Bibliographic record
Abstract
Mentorship plays an important role in supporting the career development of health leaders. An examination of mentorship programs in different organizational settings provides a frame of reference to discuss and explore personal and professional mentorship experiences. Specifically, between October 2015 and April 2016, the Emerging Health Leaders (EHL) National Health Leadership Conference (NHLC) working group collaborated on an environmental scan of mentorship programs and activities to understand innovations in mentorship. In April 2016, EHL Toronto developed a mentor feedback survey using the LEADS in a Caring Environment framework to capture the varied experiences of mentors engaged in EHL Toronto's past mentorship events. A summary of this data presented at the 2016 NHLC situates a discussion on the highly interconnected and iterative nature of mentorship and leadership development in career progression. Mentorship is seen as a continuous journey of discovery, shared learning, and personal and professional development to achieve leadership excellence.
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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 | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".