WIC Online Shopping: Challenges and Opportunities for Vendors and Farmers
Bibliographic record
Abstract
OBJECTIVE: This study aimed to expand knowledge and develop a list of technical assistance needs related to the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) online shopping for small, tribal, and rural vendors; farmers; and farmers' markets. DESIGN: A modified Delphi method was used, collecting information via a web-based survey, facilitated workshops, a listening session, and individual interviews from October 2023 to February 2024. SETTING: Surveys were distributed through Qualtrics. Zoom was used to conduct virtual workshops, a listening session, and individual interviews. PARTICIPANTS: Purposive sampling techniques were used to recruit the study sample. Of the 435 invited, 239 completed 50% of the survey; 22 survey respondents attended the workshop; 3 tribal vendors and/or farmers attended interview sessions; and 5 non-WIC-authorized vendors, farmers, and farmers' markets attended the listening session. MAIN OUTCOME MEASURE(S): The overall phenomenon of interest included themes related to facilitators and barriers to WIC online shopping. ANALYSIS: Statistical software was used to report descriptive statistics. A rapid qualitative analysis inductive approach was used to construct themes for the study. RESULTS: Facilitators included support and guidance, a robust platform, and feedback mechanisms, whereas barriers included time, resources, and technology. CONCLUSIONS AND IMPLICATIONS: To ensure WIC online shopping success, it is essential to address both the barriers and facilitators experienced by vendors, farmers, and farmers' markets. While this study collected various perspectives and experiences from merchants, future research may also focus attention on WIC participant perceptions of shopping online with these food retailers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".