From the Lalonde Report to the structural determinants of health
Bibliographic record
Abstract
Abstract In this commentary, I discuss the history and legacy of Marc Lalonde’s ground-breaking report on health promotion and critically assess how far we have come in building a society that produces health, not sickness. In doing so, I will share the arc that I see between the Lalonde Report and our new Strategic Initiative at CIHR’s Institute of Population and Population Health (IPPH), Moving Upstream, which focuses on the structural determinants of health. I propose that Moving Upstream can help us move the needle on equitable health promotion research, policy, and practice in Canada, thus building on the foundation created by Lalonde. Résumé Dans ce commentaire, j'aborde l'histoire et l'héritage du rapport de Marc Lalonde sur la promotion de la santé, et j'évalue de manière critique les progrès accomplis dans l'édification d'une société axée sur la santé, et non sur la maladie. Ce faisant, je présenterai le lien que je vois entre le rapport Lalonde et notre nouvelle initiative stratégique à l'Institut de la santé et des populations (ISPP) des IRSC, «Agir en Amont», qui développe la recherche sur les déterminants structurels de la santé. Je propose que «Agir en Amont» puisse nous aider à faire progresser la recherche, les politiques et les pratiques en matière de promotion de la santé au Canada, en nous appuyant sur les bases posées par Lalonde.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".