The Impact of Changing Medicaid Enrollments on New Mexico's Immunization Program
Bibliographic record
Abstract
BACKGROUND: Immunizations are an important component to pediatric primary care. New Mexico is a relatively poor and rural state which has sometimes struggled to achieve and maintain its childhood immunization rates. We evaluated New Mexico's immunization rates between 1996 and 2006. Specifically, we examined the increase in immunization rates between 2002 and 2004, and how this increase may have been associated with Medicaid enrollment levels, as opposed to changes in government policies concerning immunization practices. METHODS AND FINDINGS: This study examines trends in childhood immunization coverage rates relative to Medicaid enrollment among those receiving Temporary Assistance for Needy Families (TANF) in New Mexico. Information on health policy changes and immunization coverage was obtained from state governmental sources and the National Immunization Survey. We found statistically significant correlations varying from 0.86 to 0.93 between immunization rates and Medicaid enrollment. CONCLUSIONS: New Mexico's improvement and subsequent deterioration in immunization rates corresponded with changing Medicaid coverage, rather than the state's efforts to change immunization practices. Maintaining high Medicaid enrollment levels may be important for achieving high childhood immunization levels.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".