Leaders' Experiences in Planning, Implementing, and Evaluating Complex Public Health Nutrition Interventions
Bibliographic record
Abstract
OBJECTIVE: To explore California local health department leaders' experiences planning, implementing, and evaluating nutrition promotion and obesity prevention programs for low-income families. DESIGN: Qualitative, cross-sectional study using semi-structured in-depth interviews and panel interviews conducted in 2015-2016. SETTING: California local health departments (LHDs) funded by the California Department of Public Health to implement Supplemental Nutrition Assistance Program-Education (SNAP-Ed). PARTICIPANTS: The authors recruited SNAP-Ed leaders from all 58 California LHDs implementing SNAP-Ed. Leaders from 49 LHDs participated: 36 in hour-long, in-depth interviews and 13 in 1 of 3 90-minute group panel interviews. PHENOMENON OF INTEREST: Processes, facilitators, and barriers connected to delivering SNAP-Ed reported by leaders in planning, implementing, and evaluating local programs. ANALYSIS: Interviews were transcribed, coded, and analyzed using Dedoose software. RESULTS: Leaders grappled with introducing, implementing, and integrating policy, systems, and environmental change interventions (PSEs). Information used to make planning decisions varied widely across LHDs. Partnership with nontraditional organizations was described as a resource- intensive, nonlinear process with recognized potential for benefit. Rural programs reported specific and different experiences compared with their urban counterparts. CONCLUSIONS AND IMPLICATIONS: Implementing new, complex interventions to improve diet and activity environments and behaviors is both exciting and challenging for local leaders. They expressed a desire for additional resources and capacity building to facilitate success, particularly related to policy, systems, and environmental change programs. Attention to the specific needs of rural counties is needed.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".