Psychometric validation of the household food insecurity access scale among Inuit pregnant women from Northern Quebec
Bibliographic record
Abstract
BACKGROUND: Globally, food insecurity is a major public health concern. In North America, it is particularly prevalent in certain sub-groups, including Indigenous communities. Although many Indigenous and remote communities harvest and share food, most food security assessment tools focus on economic access. This study describes the psychometric evaluation of a modified Household Food Insecurity Access Scale (HFIAS), developed for mixed economies, to assess food insecurity among pregnant Inuit women. METHODS: The HFIAS was administered to 130 pregnant women in Nunavik (Arctic region of Quebec), Canada. Data were fit to a Rasch Rating Scale Model (RSM) to determine the discrimination ability of the HFIAS. Person parameter (Theta) estimates were calculated based on the RSM to provide a more accurate scoring system of the modified HFIAS for this population. Theta values were compared to known correlates of food insecurity. RESULTS: Comparative fit indices showed preference for a modified version of the HFIAS over the original. Theta values displayed a continuum of severity estimates and those values indicating greater food insecurity were consistently linked to known correlates of food insecurity. Participants living in households with more than 1 hunter (Theta = -.45) or more than 1 fisher (Theta = -.43) experienced less food insecurity than those with no hunters (Theta = .48) or fishers (Theta = .49) in their household. The RSM indicated the scale showed good discriminatory ability. Subsequent analyses indicated that most scale items pertain to the classification of a household as moderately food insecure. CONCLUSIONS: The modified HFIAS shows potential for measuring food insecurity among pregnant women in Nunavik. This is an efficient instrument that can inform interventions targeting health conditions impacting groups that obtain food through both monetary and non-monetary means.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".