Women, Infants, and Children Cash Value Voucher (CVV) Use in Arizona: A Qualitative Exploration of Barriers and Strategies Related to Fruit and Vegetable Purchases
Bibliographic record
Abstract
OBJECTIVE: Women, Infants, and Children (WIC) cash value vouchers (CVV) have been inconsistently redeemed in Arizona. The objective of this study was to explore perceived barriers to use of CVV as well as strategies participants use to overcome them. DESIGN: Eight focus groups were conducted to explore attitudes and behaviors related to CVV use. SETTING: Focus groups were conducted at 2 WIC clinics in metro-Phoenix, AZ. PARTICIPANTS: Participants in WIC who were at least 18 years of age and primarily responsible for buying and preparing food for their households. PHENOMENON OF INTEREST: Perceived barriers to CVV use and strategies used to maximize their purchasing value. ANALYSIS: Transcripts were analyzed using a general inductive approach to identify emergent themes. RESULTS: Among 41 participants, multiple perceived barriers emerged, such as negative interactions in stores or confusion over WIC rules. Among experienced shoppers, WIC strategies also emerged to deal with barriers and maximize CVV value, including strategic choice of times and locations at which to shop and use of price-matching, rewards points, and other ways to increase purchasing power. CONCLUSIONS AND IMPLICATIONS: Arizona WIC participants perceived barriers that limit easy redemption of CVV. Useful strategies were also identified that could be important to explore further to improve WIC CVV purchasing experiences.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".