Bibliographic record
Abstract
Former Scientific Advisor to the Alzheimer Society of Canada, Professor Chambers’ research career has contributed to our understanding of many issues for people living with dementia. As a leader of the Canadian Study on Health and Aging, this was the first nation-wide Canadian population study of dementia prevalence, incidence, and caregiver issues by following the health trajectory of 10,000 older adult Canadians for 10 years. Professor Chambers co-led with the award-winning community wide program to prevent cardiovascular disease, a major cause of dementia. The Cardiovascular Health Awareness Program has received awards from the British Medical Journal, Canadian Institutes for Health Research, American Heart Association, and the Canadian Medical Association Journal. Professor Chambers’ work on evaluating the effectiveness of community screening for the signs of dementia has received international recognition. With his colleagues, he has created E-learning education resources to promote interprofessional education with physicians, pharmacists, nurses, and nurse practitioners in care facilities, the main location of people living with advanced dementia in our communities. For these groups, he has created greater access to library services, establishment of a system-wide seniors’ health knowledge network, as well as promotion of partnerships between academic and service delivery organizations such as care homes. Early in his career, Professor Chambers was an international scientific exchange fellow with the Research Unit on Neuropsychiatry: Epidemiology and Clinical Research, INSERM (medical research council), University of Montpellier, France. He has served on expert panels for Health Canada, US Institute of Medicine, WHO, and Pan American Health Organization. He has authored 18 books, 21 chapters in books, and 180 papers in refereed journals. Professor Chambers presently is Research Director of the Niagara Regional Campus, Michael G. DeGroote School of Medicine, McMaster University. He maintains appointments with the Department of Research Methods, Evidence, and Impact, McMaster University (Professor Emeritus); as well as with the Bruyère Research Institute., Faculty of Health at York University and IC/ES, Ontario’s leading health and social data research organization. From 2013 to 2017, he was Scientific Advisor to the Alzheimer Society of Canada. He is a Fellow with the American College of Epidemiology, Honorary Fellow with the Faculty of Public Health of the United Kingdom and Fellow of the Canadian Academy of Health Sciences.
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 | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.298 | 0.147 |
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".