Is public health training in Canada meeting current needs? Defrosting the paradigm freeze to respond to the post-truth era
Bibliographic record
Abstract
Health scholars have long been calling for a new approach to understanding and responding to public health challenges, recognizing the dynamic influence of social and ecological processes and the importance of respecting different ways of knowing. With daunting new challenges to collective health, we sought to ascertain how future generations of public health researchers and practitioners are being prepared with the knowledge, attitudes, and skills needed for the tasks ahead. We found that of the 76 graduate level programs listed by the Public Health Agency of Canada, 65% required at least one quantitative methods course, but only 26% required qualitative methods and only 16% required a course in community engagement. While 25% had at least one required course related to social theory or social determinants of health, only 3% required a course on the ecological determinants. Our examination suggests that the majority of schools of public health may still be frozen in old paradigms wherein interdisciplinary inquiry and the development of skills to work with communities to implement and evaluate interventions to promote and protect collective health are still only peripheral considerations. With the intensification of public distrust in experts in this post-truth era, greater emphasis is needed now more than ever to develop skills in understanding and engaging the public in addressing the underlying issues threatening health. We argue that as the challenges of the Anthropocene are upon us, it is urgent that we rethink the skills we are teaching and prepare ourselves to radically adjust our approach.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.015 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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".