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Record W2758790055 · doi:10.2105/ajph.2017.304033

Diagnostic Accuracy of Two Food Insecurity Screeners Recommended for Use in Health Care Settings

2017· article· en· W2758790055 on OpenAlexaboutno aff
Jennifer A. Makelarski, Emily Abramsohn, Jasmine H. Benjamin, Senxi Du, Stacy Tessler Lindau

Bibliographic record

VenueAmerican Journal of Public Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersNational Institute on AgingNational Institutes of HealthUniversity of ChicagoNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of Chicago MedicineAcademyHealth
KeywordsMedicineGold standard (test)RecallConfidence intervalQuarter (Canadian coin)Public healthFood insecurityEmergency departmentEnvironmental healthHealth carePediatricsFood securityDemographyPsychiatryInternal medicinePsychologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: To test the diagnostic accuracy of the American Academy of Pediatrics (AAP) recommended food insecurity screener. METHODS: We conducted prospective diagnostic accuracy studies between July and November 2016 in Chicago, Illinois. We recruited convenience samples of adults from adult and pediatric emergency departments (12-month recall study: n = 188; 30-day recall study: n = 154). A self-administered survey included the 6-item Household Food Security Screen (gold standard), the validated 2-item Hunger Vital Sign (HVS; often, sometimes, never response categories), and the 2-item AAP tool (yes-or-no response categories). RESULTS: Food insecurity was prevalent (12-month recall group: 46%; 30-day group: 39%). Sensitivity of the AAP tool using 12-month and 30-day recall was, respectively, 76% (95% confidence interval [CI] = 65%, 85%) and 72% (95% CI = 57%, 84%). The HVS sensitivity was significantly higher than the AAP tool (12-month: 94% [95% CI = 86%, 98%; P = .002]; 30-day: 92% [95% CI = 79%, 98%; P = .02]). CONCLUSIONS: The AAP tool missed nearly a quarter of food-insecure adults screened in the hospital; the HVS screening tool was more sensitive. Public health implications. Health care systems adopting food insecurity screening should optimize ease of administration and sensitivity of the screening tool.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.308
GPT teacher head0.516
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations101
Published2017
Admission routes1
Has abstractyes

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