The Four Domain Food Insecurity Scale (4D-FIS): development and evaluation of a complementary food insecurity measure
Bibliographic record
Abstract
The U.S. Department of Agriculture (USDA) Food Security Survey Module (FSSM) is a valuable tool for measuring food insecurity, but it has limitations for capturing experiences of less severe food insecurity. To develop and test the Four Domain Food Insecurity Scale (4D-FIS), a complementary measure designed to assess all four domains of the food access dimension of food insecurity (quantitative, qualitative, psychological, and social).Low-income Black, Latina, and White women (n = 109) completed semi-structured (qualitative) and structured (quantitative) interviews. Interviewers separately administered two food insecurity scales, including the 4D-FIS and the USDA FSSM adult scale. A scoring protocol was developed to determine food insecurity status with the 4D-FIS. Analyses included a confirmatory factor analysis to examine the hypothesized structure of the 4D-FIS and an initial evaluation of reliability and validity. A four-factor model fit the data reasonably well as judged with fit indices. Results showed relatively high factor loadings and inter-factor correlations indicated that factors were distinct. Cronbach's alpha (ɑ) for the overall scale was 0.90 (subscale ɑ ranged from 0.69 to 0.91) and provided support for the scale's internal consistency reliability. There was fair overall agreement between the 4D-FIS and USDA FSSM adult scale, but agreement varied by category. Findings provide preliminary support for the 4D-FIS as a complementary measure of food insecurity, with implications for researchers, practitioners, and policymakers working in U.S. communities.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".