Systematic Review of Factors Influencing Farmers’ Market Use Overall and among Low-Income Populations
Bibliographic record
Abstract
BACKGROUND: Recent evidence indicates a widening gap in fruit and vegetable (F/V) consumption between high- and low-income Americans. This gap is related, in part, to decreased access to food retailers that sell fresh F/V in low-income communities. Farmers' markets are identified as a strategy for improving F/V consumption by increasing access to these foods. OBJECTIVES: The aim of this systematic review was to examine literature published from 1994 to 2014 to identify facilitators and barriers of farmers' markets use, particularly among low-income consumers. DESIGN: Peer-reviewed literature was identified in Ebsco Host (Academic Search Complete). Inclusion criteria for abstract review was primary research focused on farmers' market use identifying 87 studies for full-text review. Full-text review identified articles focused on facilitators and/or barriers of farmers' market use resulting in 49 articles. At least two reviewers completed review of all articles. RESULTS: Of the 49 articles, 39% specified inclusion of low-income consumers and fewer than 15% focused on racial and ethnic minorities. Few studies were guided by theory and/or used standardized metrics. Results indicate farmers' market use is influenced by multiple economic, service delivery, spatial-temporal, social, and personal factors. Among studies that included low-income populations (n=19), key barriers to farmers' market use were perceptions that food assistance benefits were not accepted, belief that food variety at farmers' markets was limited, lack of access to transportation, lack of racial/ethnic diversity in the market space, and mismatch between markets and personal lifestyles. There is wide variation in study design and reporting standards and infrequent use of standardized measures limiting comparisons across studies. CONCLUSIONS: There is a need to establish valid and reliable metrics and reporting standards for evaluating farmers' markets. Findings may inform interventions, programs, and policies to promote farmers' market use.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".