Screening and addressing food insecurity at free clinics: a scoping review
Bibliographic record
Abstract
Food insecurity (FI) is highly prevalent amongst patients seeking care at free, student-run health clinics. This study sought to examine the existing literature of food insecurity screenings and interventions at free clinics across the U.S. In this review, we provide the rate of FI screenings, the prevalence of FI, demographic information of patients screened, and interventions and barriers faced by clinics while implementing interventions to improve FI. Studies included in this review needed to implement a FI screening and intervention program in a free clinic within the United States. Additionally, eligible studies could address additional social determinants of health or chronic conditions, but had to include FI screenings and interventions. Due to the inclusion criteria requiring studies to occur in student-run free clinics, of the initial database search of 958 studies, only five were included for analysis. Among the studies, food insecurity was determined by the 6 item US Household Food Security Scale(HFSS), a single question from the USDA food security survey, and a custom 12 item redcap survey. Methods of combating food insecurity included grocery deliveries, in-clinic food pantries, onsite food boxes, aiding patients in accessing SNAP, WIC, and food pantries in the area, and providing referrals to community specific programs who provide food aid. General trends show that implemented FI interventions can range from barrier-informed support to general on-site food pantries. However, our review further showed that the lack of assessment of intervention outcomes limits conclusions on their respective effectiveness. Moving forward, future studies should focus on evaluating implemented programs to improve their broader applicability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".