On Living a Long, Healthy, and Happy Life, Full of Love, and with no Regrets, until Our Last Breath
Bibliographic record
Abstract
Professor Jadad is a physician, educator, researcher, and public advocate, whose mission is to help improving health and wellness for all, through human networks fueled by innovative uses of information and communication technologies. He has been called a ‘human Internet', as his research and innovation work seeks to identify and connect the best minds, the best knowledge, and the best tools across traditional boundaries to eliminate unnecessary suffering. Such work focuses on a radical ‘glocal' innovation model designed to improve the capacity of humans to imagine, to create, and to promote new and better approaches for living, healing, working, and learning across the world. Powered by social networks and other leading-edge telecommunication tools, his projects attempt to anticipate and respond to major public health threats (e.g., multiple chronic conditions, pandemics) through strong and sustainable international collaboration, and to enable the public (particularly young people) to shape the health system and society. Alejandro Jadad holds various positions at the University of Toronto and the University Health Network, all related to the creation and optimization of human health. He is the Canada Research Chair in eHealth Innovation; founder of the Centre for Global eHealth Innovation; Senior Scientist at the Centre for Health, Wellness and Cancer Survivorship (ELLICSR); and Professor at the Departments of Anesthesia, Faculty of Medicine, and at the Dalla Lana Faculty of Public Health. The interview was conducted by Professor Claus Vögele.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.035 | 0.028 |
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".