Culturally-Informed Food Insecurity Screening: Evidence From Northern Navajo Medical Center
Bibliographic record
Abstract
OBJECTIVE: To develop and evaluate an alternative strengths-based and culturally-informed screener for food security among American Indian respondents. DESIGN: Two-phase mixed methods sequential exploratory study with a qualitative phase followed by a quantitative phase. SETTING: Northern Navajo Medical Center. PARTICIPANTS: Twenty-five qualitative participants and 97 quantitative participants. PHENOMENON OF INTEREST: Participants' impressions of the existing food insecurity screener compared with an alternative set of questions focused on the type and amount of food consumed. ANALYSIS: Cognitive interviews were thematically analyzed and integrated with mixed methods in developing the quantitative survey, and survey responses on the 2 food insecurity screening questionnaires were analyzed for equivalent-form reliability with a Pearson correlation coefficient. RESULTS: The prevalence of food insecurity was high. Interviewees had concerns about the existing screener. Respondents provided suggestions for alternative food security questions. When feedback was integrated into the alternative screener for the quantitative phase, the correlation between screeners was weak to moderate (0.3). CONCLUSIONS AND IMPLICATIONS: Findings suggest the screeners may be collecting data related to unique concepts. Rampant food insecurity supports the need for future food access initiatives in the Navajo Nation to provide a foundation for a food-secure future.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".