Patterns and correlates of use of evidence-based interventions to control diabetes by local health departments across the USA
Bibliographic record
Abstract
OBJECTIVE: The nearly 3000 local health departments (LHDs) nationwide are the front line of public health and are positioned to implement evidence-based interventions (EBIs) for diabetes control. Yet little is currently known about use of diabetes-related EBIs among LHDs. This study used a national online survey to determine the patterns and correlates of the Centers for Disease Control and Prevention Community Guide-recommended EBIs for diabetes control in LHDs. RESEARCH DESIGN AND METHODS: A cross-sectional study was conducted to survey a stratified random sample of LHDs regarding department characteristics, respondent characteristics, evidence-based decision making within the LHD, and delivery of EBIs (directly or in collaboration) within five categories (diabetes-related, nutrition, physical activity, obesity, and tobacco). Associations between delivering EBIs and respondent and LHD characteristics and evidence-based decision making were explored using logistic regression models. RESULTS: Among 240 LHDs there was considerable variation among the EBIs delivered. Diabetes prevalence in the state was positively associated with offering the Diabetes Prevention Program (OR=1.28 (95% CI 1.02 to 1.62)), diabetes self-management education (OR=1.32 (95% CI 1.04 to 1.67)), and identifying patients and determining treatment (OR=1.27 (95% CI 1.05 to 1.54)). Although all organizational supports for evidence-based decision making factors were related in a positive direction, the only significant association was between evaluation capacity and identifying patients with diabetes and determining effective treatment (OR=1.54 (95% CI 1.08 to 2.19)). CONCLUSION: Supporting evidence-based decision making and increasing the implementation of these EBIs by more LHDs can help control diabetes nationwide.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".