Taking a Stand to Remedy the Inadequacies of Action on Health Equity Exposed by COVID-19
Bibliographic record
Abstract
As we struggle with the impacts of a global pandemic, there is growing evidence of the inequitable impacts of this crisis. In this commentary, we argue that actions on health equity to date have been insufficient despite significant scholarship to guide both practice and policy. To move from talk to action on health equity, we propose the following five approaches: (1) reversing the erosion of publicly funded health systems; (2) creating broad economic means to support health; (3) moving health action upstream; (4) challenging ageist and/or ableist discourses; and (5) decolonizing approaches and enacting solidarity. Engaging in these actions will help close the gaps and address disparities made more evident during this global pandemic. The COVID-19 pandemic reinforces the need for us to move from discussion to action if we are to achieve health for all. Adopting a health equity lens is a means of both understanding and stimulating action to readdress the root causes of inequities and work toward a fairer, more just society.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.049 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.043 | 0.066 |
| Insufficient payload (model declined to judge) | 0.004 | 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".