Extramural US Federal Research Grants For Health Outcomes Associated With Climate Change Inadequate, Too Narrow In Focus
Bibliographic record
Abstract
Climate change causes and exacerbates disease, creates and worsens health disparities, disrupts health care delivery, and imposes a significant disease burden in the US and globally. Critical knowledge gaps hinder an evidence-based response and are perpetuated by scarce federal research funds. We identified and described extramural US federal research funding (that is, grants provided to organizations and institutions outside of federal agencies) that both addressed health outcomes associated with climate change and was awarded between 2010 and 2020. During this eleven-year period, 102 grants met our criteria, totaling approximately $58.7 million, or approximately $5.3 million per year (2020 adjusted US dollars). Federal investments in climate change and health research during this period failed to address the breadth of climate-sensitive exposures, health outcomes, and impacts on vulnerable populations. Moving forward, in addition to increasing investment in climate and health research across all known hazards, critical attention should be placed on vulnerable populations and health equity. To achieve this, increased federal research coordination and cooperation are needed, as well as a mechanism to track this funding.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".