Contextual Factors Influencing the Adoption of Physical Activity Direct Education and Policy, Systems, and Environmental Change Initiatives by Virginia EFNEP and SNAP-Ed Staff
Bibliographic record
Abstract
OBJECTIVE: To explore factors influencing the adoption of direct education programs and policy, systems, and environmental (PSE) change initiatives focused on physical activity for Supplemental Nutrition Assistance Program-eligible audiences by Virginia Expanded Food and Nutrition Education Program and Supplemental Nutrition Assistance Program-Education (SNAP-Ed) staff. METHODS: Online survey with Expanded Food and Nutrition Education Program and SNAP-Ed peer (paraprofessional) educators (n = 28) and SNAP-Ed agents (master of science level) (n = 9) in Virginia. Descriptive statistics were computed for sociodemographic characteristics and responses to questions on the basis of Likert-type scales. Exploratory factor analyses were run to identify the underlying structures of the different variables. RESULTS: The main factors for peer educators were related to substituting nutrition programs or content for physical activity programs. Other factors included staff qualifications and expectations about leading vs teaching physical activities. For PSEs, the top factors were the capacity to reach many community members, attract new partners and stakeholders, and personal interest in the PSE. CONCLUSIONS AND IMPLICATIONS: The results provide insight into potential barriers and motivators for adopting physical activity education and PSEs within community-based initiatives and can be used to inform program planning and staff training. Additional research is warranted to examine other factors influencing the adoption and implementation of physical activity programs and PSEs.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".