A Mixed Methods Approach to Investigating Food Safety Behavior in a Sample of Native American and Hispanic Caregivers of Young Children
Bibliographic record
Abstract
Foodborne illness (FBI) disproportionately affects children and minority populations in the U.S. A mixed methods convergence model design was used to explore the food safety knowledge and behavior of Native American (NA) and Hispanic (Hisp) caregivers in New Mexico who prepare food for young children in the home. Quantitative and qualitative research methods (a validated food safety knowledge survey (r=.793) and focus group interviews) were implemented in parallel within each ethnic group, the datasets were analyzed separately per group and the results were converged at the point of interpretation. Equal priority was given to each dataset type. The Health Belief Model was used as a theoretical framework to guide qualitative inquiry. An integrative summary of the quantitative and qualitative results was created and meta-inferences identified contradictory and confirmatory elements of the evidence across both groups. A purposeful sample of fifty-five participants in New Mexico (28 NA; 27 Hisp) completed the food safety knowledge survey and participated in focus groups. Quantitative composite mean scores for the Native American (NA) group (M=66%) and Hispanic (His) group (M=65%) indicated low food safety knowledge. A MANOVA conducted to compare the two groups’ mean knowledge scores found no significant difference between groups on the food safety subscales [Wilks’ ? = .852, F(6,44) = 1.278, p = .287, ?2 = .162].The lowest scoring subscale for both groups was ‘cook’, addressing proper cooking methods (NA=.61, Hisp=.55). Mixed methods analyses revealed that participants overall perceived moderate to high self-efficacy regarding safe food preparation, food purchasing, cooking food, and storing of food, however, the related food safety knowledge item scores were low. Food safety knowledge was often inconsistent with reported food safety practices. Moderate/high self-efficacy may provide a false sense of low risk for FBI.
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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 | 0.045 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".