A cautionary tale: university institutes of public health must “walk the talk”
Bibliographic record
Abstract
Public health is, and has long been, defined as the art and science of promoting health and preventing illness through organized efforts of society.This definition and scope convey certain elements and values, which distinguish public health from other fields of scholarship and practice (CPHA, 2017).These include population-level thinking, a commitment to health equity and its foundations in social justice, and an upstream focus on root causes of inequities, which are embedded within political and economic systems.Important work in recent years has highlighted the weakening of these core elements of public health, in Canada and elsewhere (Yong, 2021).Hancock et al. (2020) and McLaren and Hennessy (2020), for example, highlight how the COVID-19 pandemic, despite bringing public health to the forefront, has reinforced a narrow, medicalized version that largely neglects decades of scholarship on intersecting social and ecological determinants of health that shape population well-being in highly inequitable ways.In the light of these trends, one might expect-or hopethat public health institutes within public universities would serve as a bastion.As institutions whose role is to build and empower informed and critically engaged citizens, a public health institute within a public university should provide a space and platform to actively push back against harmful trends that are eroding the foundations that support the core values and actions within our field.
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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 | 0.038 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.036 | 0.098 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".