Perceived Benefits and Challenges of Coordinated Approaches to Chronic Disease Prevention in State Health Departments
Bibliographic record
Abstract
INTRODUCTION: Chronic disease prevention efforts have historically been funded categorically according to disease or risk factor. Federal agencies are now progressively starting to fund combined programs to address common risk. The purpose of this study was to inform transitions to coordinated chronic disease prevention by learning views on perceived benefits and challenges of a coordinated approach to funding. METHODS: A national survey on evidence-based public health was conducted from March through May 2013 among state health department employees working in chronic disease prevention (N = 865). Participants were asked to rank the top 3 benefits and top 3 challenges in coordinating chronic disease approaches from provided lists and could provide additional responses. Descriptive analyses, χ(2) tests, and analysis of variance were conducted. RESULTS: The most common perceived benefits of coordinated approaches to chronic disease prevention were improved health outcomes, common risk factors better addressed, and reduced duplication of program efforts. The most common perceived challenges were funding restrictions, such as disease-specific performance measures; competing priorities; lack of communication across programs; funding might be reduced; agency not structured for program coordination; and loss of disease-specific partner support. Rankings of benefits and challenges were similar across states and participant roles; the perceived challenges "lack of communication across programs" (P = .02) and "funding might be reduced" differed by program area (P < .001). CONCLUSION: Findings can be used by funding agencies and state health departments for planning, training, and technical assistance. The information on perceived challenges demonstrates the need to improve communication across programs, enhance organizational support for coordinated approaches, and create benefits for organizational partners.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".