Merck launches new grant program to boost health globally
Bibliographic record
Abstract
Pharmaceutical firm Merck, known as MSD outside of the U.S. and Canada, has launched a new global grants program, Solutions for Healthy Communities, that will support the work of nonprofits dedicated to improving the well-being of underserved populations in communities around the world. According to the company, SHC will invest in strategies that are designed and led by local stakeholders to meet local health needs and priorities. Grants will cover two years of project implementation, and awards will range in size from $50,000–300,000 each. The initiative will aim to catalyze innovation and facilitate access to quality healthcare, investing in programs that serve populations that are historically underserved by the healthcare system, including black, indigenous, and other people of color; people experiencing poverty; people living in rural areas; migrant populations; people with diverse gender identities and/or sexual orientations; and people living with disabilities, Merck said. SHC grants will be available in all of the regions in which the company operates, including the U.S., Europe, the Middle East, Africa, Canada, Latin America and Asia Pacific. Priority will be given to nonprofits that operate within 50 miles of a company site, the company said. For more information about the grant program, visit https://www.merck.com.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.156 | 0.052 |
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".