The bio-psycho-social determinants of health: Reflections on the CIAR Population Health Program (1987–2003)
Bibliographic record
Abstract
In the mid-1980s, a remarkable polymath, Dr Fraser Mustard, founded the Canadian Institute for Advanced Research (now CIFAR - https://cifar.ca/ ), as a unique way to develop, across Canadian and selected international universities, over a dozen multi-disciplinary groups of researchers tackling major intellectual challenges of that era. Among these groups was the Population Health Program (PHP), led for its initial decade by the brilliant Canadian health economist Prof Bob Evans. Over the next decade-and-a-half, the PHP met nearly fifty times with top international scholars in all types of health research. Out of these interactions, the Program's membership formulated a set of ideas about how the health of entire human populations and societies is determined, as well as enunciated clear policy and program implications of those ideas. This paper summarizes, from the point of view of the author (a Scholar, then Fellow in the PHP) the main themes that the PHP enunciated during its initial decade, culminating in the widely read volume "Why are Some People Healthy and Others Not"?. Finally, developments in the field of Population Health in the two decades since the "sunsetting" of the PHP are reviewed and commented upon.
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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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".