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The Effectiveness of Food Insecurity Screening in Pediatric Primary Care

2014· article· en· W1987666335 on OpenAlexvenueno aff
Wendy G. Lane, Howard Dubowitz, Susan Feigelman, Gina Poole

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2014
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsMedicineFood insecurityPrimary careEnvironmental healthFood securityFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Food insecure children are at increased risk for medical and developmental problems. Effective screening and intervention are needed. METHODS: Our purpose was to (1) evaluate the validity and stability of a single item food insecurity (FI) screen. (2) Assess whether use may lead to decreased FI. Part of a larger cluster randomized controlled trial, pediatric residents were assigned to SEEK or control groups. A single FI question (part of a larger questionnaire) was used on SEEK days. SEEK residents learned to screen, assess, and address FI. A subset of SEEK and control clinic parents was recruited for the evaluation. Parents completed the USDA Food Security Scale ("gold standard"), upon recruitment and 6-months later. Validity, positive and negative predictive values (PPV, NPV) was calculated. The proportion of screened families with initial and subsequent FI was measured. Screening effectiveness was evaluated by comparing SEEK and control screening rates and receipt of Supplemental Nutrition Assistance Program (SNAP) benefits between initial and 6-month assessments. RESULTS: FI screen stability indicated substantial agreement (Cohen's kappa =0.69). Sensitivity and specificity was 59% and 87%, respectively. The PPV was 70%; NPV was 81%. SEEK families had a larger increase in screening rates than control families (24% vs. 4.1%, p<0.01). SEEK families were more likely to maintain SNAP enrollment (97% vs. 81%, p=0.05). FI rates remained stable at approximately 30% for both groups. CONCLUSIONS: A single question screen can identify many families with FI, and may help maintain food program enrollment. Screening may not be adequate to alleviate FI.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.041
GPT teacher head0.388
Teacher spread0.347 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations46
Published2014
Admission routes1
Has abstractyes

Explore more

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