Abstract 6936: American versus Canadian National Funding for Immigrant Cardiovascular Health
Bibliographic record
Abstract
Introduction: The high number of immigrants in North America underscores the need for cardiovascular (CV) health and promotion in these racially and ethnically diverse populations. Although some immigrant races/ethnicities have high cardiovascular mortality, it remains unclear whether there are investments in cardiovascular health promotion and prevention for immigrants for both the United States and America. We aimed to compare American and Canadian national investments cardiovascular health promotion and prevention for immigrants. Methods: We queried the National Institute of Health (NIH) and the Canadian Institute of Health Research (CIHR) databases from 2006 to 2019 for grants supporting projects in diseases prevention and promotion targeting immigrants. We compiled annual funding and normalized the annual data for the number of immigrants and Gross Domestic Product (GDP). We completed normalized descriptive statistics for comparative analysis and presented funding trends over a 14-year period. Results: There were 74 and 50 project grants awarded by the NIH and CIHR, respectively. Between 2007 to 2013, the NIH spent relatively less per immigrant than the CIHR. From 2014 onward, funding trends reversed as the NIH outspent the CIHR. Over the 14-year period, the NIH and the CIHR funded an annual mean of $59.8/1000 immigrants and $79.1/1000 immigrants, respectively. Conclusion: Although the number of immigrants is rising, the relative national funding for CV health promotion and prevention in the USA and Canada is generally decreasing over the last 5 years. Absolute CIHR funding is progressively decreasing since 2008, while NIH grant expenditure has demonstrated an overall pattern of increase over the last 14 years. Policymakers should consider more comprehensive investment in CV health promotion and prevention for immigrants.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".