Bibliographic record
Abstract
Health promotion has long aspired for a world where all people can live to their full potential. Yet, COVID-19 illuminates dramatically different consequences for populations bearing heavy burdens of systemic disadvantage within countries and between the Global South and Global North. Many months of pandemic is entrenching inequities that reveal themselves in the vastly differential distribution of hospitalization and mortality, for example, among racialized groups in the USA. Amplified awareness of the intimate relationship between health, social structures, and economy opens a window of opportunity to act on decades of global commitments to prioritize health equity. Choices to act (or not act) are likely to accelerate already vast inequities within and between countries as rapidly as the COVID-19 pandemic itself. Recognizing the inherently global nature of this pandemic, this article explores how determinants of equity are embedded in global responses to it, arguing that these determinants will critically shape our global futures. This article aims to stimulate dialogue about equity-centered health promoting action during a pandemic, using the Canadian Coalition for Global Health Research (CCGHR) Principles for Global Health Research to examine equity considerations at a time of pandemic. Attentiveness to power and the relationship between political economy and health are argued as central to identifying and examining issues of equity. This article invites dialogue about how equity-centered planning, decision-making and action could leverage this massive disruption to society to spark a more hopeful, just, and humane collective future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".