Bibliographic record
Abstract
OBJECTIVE: We predicted the amount of health outcome improvement any state might achieve if it could reach the highest level of key health determinants any individual state has already achieved. METHODS: Using secondary county-level data on modifiable and nonmodifiable health determinants from 1994 to 2003, we used regression analysis to predict state age-adjusted mortality rates in 2000 for those younger than age 75, under the scenario of each state's "ideal" predicted mortality if that state had the best observed level among all states of modifiable determinants. RESULTS: We found considerable variation in predicted improvement across the states. The state with the lowest baseline mortality, New Hampshire, was predicted to improve by 23% to a mortality rate of 250 per 100,000 population if New Hampshire had the most favorable profile of modifiable health determinants. However, West Virginia, with a much higher baseline, would be predicted to improve the most-by 46% to 254 per 100,000 population. Individual states varied in the pattern of specific modifiable variables associated with their predicted improvement. CONCLUSIONS: The results support the contention that health improvement requires investment in three major categories: health care, behavioral change, and socioeconomic factors. Different states will require different investment portfolios depending on their pattern of modifiable and nonmodifiable determinants.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".