Health Promotion in Canada: perspectives & future prospects - doi:10.5020/18061230.2007.p3
Bibliographic record
Abstract
Thank-you for the opportunity to be with you today in this fascinating panel on the state of health promotion in Brazil, Canada and around the world. It is a great pleasure to be here, and to share my thoughts and reflections with you, not as na expert here to tell you how it ‘should’ be, but as a colleague interested in dialogue around points of mutual concern. I feel we have much to learn from what has been happening here in Brazil, and the work of Paolo Freire and many contemporary colleagues who continue this tradition of critical pedagogy for health (like my colleague and friend here at UNIFOR, Dr. Francisco Cavalcante Jr.). So in this spirit of friendship, dialogue and mutual learning, I will be very frank with you about the lessons learned in Canada, including some of our failures and mistakes which I hope you can successfully avoid. Also, I offer my apologies for not being able to speak with you in your own language. I wish to thank my friends Nicolas Ayres and Francisco Cavalcante Jr. For their assistance with translation. In addition to a brief overview of the development of health promotion in Canada, I would like to share some reflections on the social, political and economic context in which the field has evolved, both in Canada and internationally. I Will address three (3) key tensions I see in the field at the moment (from a Canadian perspective), and reflect on our successes and our failures. I will close with a few thoughts on future prospects and some of the challenges that I see that lie ahead. I would like to emphasize that any brief history of health promotion in Canada, and any assessment of its strengths, contributions and failures is inherently ‘subjective’ and idiosyncratic. Rather than repeat the work of other analysts and commentators (see for example – cite PHAC/HC docs), I offer my observations based on over a decade of involvement in the field (including involvement in the Critical Social Science and Health group at the University of Toronto), and in my capacity as Director of the Masters of Health Science program in health promotion at the University of Toronto. Doubtless, those with different interests, orientations, and practice backgrounds would come to (slightly or substantially) different conclusions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.161 | 0.021 |
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".