10 min with Mr Jeff Mainland, Executive Vice-President of the Hospital for Sick Children, Toronto, Ontario, Canada
Bibliographic record
Abstract
### Biography Mr Jeff Mainland, BSc, MBA is the Executive Vice-President at the Hospital for Sick Children in Toronto, Ontario, Canada. He has nearly 20 years of experience in leadership at Canadian paediatric academic health centres. Starting his career in Nuclear Medicine, Mr Mainland gained invaluable clinical experience working with patients and their families. He has since held several leadership roles in the public sector, including Executive Officer at the Office of the Chief Coroner, and Chief of Staff to the Deputy Premier for the Province of Ontario. Mr Mainland is an accomplished healthcare leader with extensive experience in quality improvement, patient safety, strategy, operations and communications. My key leadership messages are: (1) pursue and embrace the power of partnerships and collaboration, (2) strive for progress over perfection, (3) become comfortable leading through ambiguity and (4) consider data as one of your biggest assets in decision-making. Leaders have had to become more comfortable with ambiguity throughout the pandemic. For example, policy directions and the science around COVID-19 have been at times unclear. With the prevalence of social media, there is constant speculation and innuendo circulating. What we have had to learn is that decision-making needs to remain flexible and processes need to remain nimble. We will not always have perfect solutions and answers readily available during these times of uncertainty. We have had to make decisions based of the best information and data that are available at the time and recognise that things may not be perfect. The Hospital for Sick Children (SickKids) is striving to become a data-driven enterprise. When possible, I use data to inform my decisions and ensure they are communicated transparently to our staff. For example, at the beginning of the COVID-19 pandemic, there was significant uncertainty over personal protective equipment (PPE) supply. We developed a dashboard of …
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.269 | 0.103 |
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".