Day Scholars Food Insecurity Experience Scale-Survey Module (DSFIES-SM): Psychometric Analysis
Bibliographic record
Abstract
Background: School feeding programs’ evaluation requires the measurement of food insecurity, a more objective indicator, within school in low-income countries. The Global Child Nutrition Foundation (GCNF) uses subjective indicators to report school feeding coverage rates across many countries that participate in the global survey of school meal programs all year round. Aim: To test the methodological feasibility of measuring a school food insecurity construct as a direct indicator of the effectiveness and efficiency of school feeding programs. Methods: Two-stage sampling was used during the selection of schools and 128 schoolchildren with a mean ( SD) age of 10.5(1.58) were recruited. Item Response Theory (IRT) and Classical Test Theory (CTT) approaches were utilized during DSFIES-SM development. Psychometric analysis was utilized to assess the psychometric properties of the measure of school food insecurity and to establish the construct-level reliability, convergent and discriminant validity of the DSFIES-SM. Results: DSFIES-SM generated acceptable item-level reliabilities, ranging from .75 to .78. The construct-level reliability of the DSFIES-SM was indicated by Cronbach’s α of .78. Composite reliability was at .77. Fit measures and tests of model fitness for confirmatory factor analysis (CFA) (RMSEA < 0.0001; TLI = 1.06; SRMR = 0.043; CFI = 1.00, p = .85) confirmed that the data fitted the model perfectly. Conclusion: DSFIES-SM consists of twelve questions with dichotomous yes/no responses. DSFIES-SM has good convergent and discriminant validity. Findings suggest that the items compose a statistical scale designed to cover a range of severity of school food insecurity. Future replications to establish other forms of validity across different cultural contexts in low-income countries can be of benefit to the present research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".