Cardiovascular Disease Risk Among the Poor and Homeless – What We Know So Far
Bibliographic record
Abstract
Homelessness [and poverty] is rapidly escalating across North America and is associated with dire implications for public health and our health care systems. Both are compelling states of existence affecting all ages, ethnicities and both genders. Homelessness frequently evolves through a complex interaction of factors that are both internal and external to the individual themselves. Once homeless, equitable access to both preventative and remedial health care is lacking and is associated with a higher than average burden of cardiovascular disease [CVD] risk factors, morbidity and mortality and is accompanied by disproportionately high health care costs. The emergence of limited, small scale programs aimed at addressing the unique health and social needs of the homeless is encouraging. However, there has been inadequate commitment at the National, State or Provincial and local levels to implement policies and dedicate funding and resources to the expansion of such "individual level" interventions into comprehensive programs that deliver sustainable, integrated prevention and services, especially with regard to CVD. The long-term solutions that address the links between homelessness and CVD lie in preventing homelessness and reversing the trends in our health care system that create disparities for lower socioeconomic status [SES] and homeless individuals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".