Virtual Program Delivery: Learning Through Extension Nutrition Educators’ Experiences During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: To describe and analyze how Extension nutrition educators in one state system transitioned from primarily face-to-face to virtual nutrition education programming. DESIGN: This exploratory case study gathered data through nutrition educator interviews, virtual program delivery guides, and nutrition educators' program impact statements. SETTING: Southeastern State Extension system in late 2022. PARTICIPANTS: The sample included 15 participant interviews, multiple virtual program delivery guides, and 43 program impact summaries. PHENOMENON OF INTEREST: The use of Cultural Historical Activity Theory as a framework to explore educators' learning process with virtual program delivery and how this learning influenced community nutrition program delivery choices. ANALYSIS: Qualitative data was analyzed with ATLAS.ti using a priori coding. RESULTS: Two key findings emerged from the data: educators were more likely to deliver programs in a virtual setting when the programs aligned with their values and skills, and educators preferred flexible program curricula and delivery guides because it allowed them to address their community's specific needs. CONCLUSIONS AND IMPLICATIONS: Educators plan to continue to deliver certain community nutrition programs virtually. Future research is needed to explore additional perspectives on virtual delivery, such as program participants and state program managers.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".