Looking back and moving forward: Addressing health inequities after COVID-19
Bibliographic record
Abstract
We will likely look back on 2020 as a turning point. The pandemic put a spotlight on existing societal issues, accelerated the pace of change in others, and created some new ones too. For example, concerns about inequalities in health by income and race are not new, but they became more apparent to a larger number of people during 2020. The speed and starkness of broadening societal conversation, including beyond the direct effects of COVID-19, create an opportunity and motivation to reassess our understanding of health. Perhaps more importantly, it is an opportunity to reduce inequities in who has access to, who uses, and who benefits from the resources that promote health and well-being. To this end, we offer three questions to guide thinking about health and health inequities after 2020: (1) what do we mean by "health" and "health inequality and inequity"? (2) what are the structures and policies we put in place to support or promote health, and how effective are they? And (3) who has the power to shape structures and policies, and whose interests do those structures and policies serve?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".