Food Stress and Diabetes-Related Psychosocial Outcomes in American Indian Communities: A Mixed Methods Approach
Bibliographic record
Abstract
OBJECTIVE: Explore the relationship between diabetes-related psychosocial outcomes and food stress in American Indian communities. DESIGN: Convergence model of a mixed methods triangulation study. SETTING: Five American Indian reservation communities in the Midwest. PARTICIPANTS: One-hundred ninety-two participants were randomly selected from tribal health centers using clinic patient records and were surveyed about diabetes distress, empowerment, and food stress across 4 different time points. Seventeen focus group discussions were conducted and transcribed, and a mix of purposive and convenience sampling was used. PHENOMENON OF INTEREST: Psychosocial outcomes associated with (or related to) diabetes and food stress. ANALYSIS: Quantitative: Multiple linear regression was performed to explore relationships between food stress and diabetes distress and empowerment. Qualitative: Open coding of data identified portions of the transcripts related to food followed by a deductive approach on the basis of the components of quantitative food stress. RESULTS: Food stress in the forms of (1) not having enough money for food and not having enough time for cooking or shopping (P = 0.08) and (2) inadequate food access and being on a special diet (P = 0.032) were associated with increased diabetes distress. Lower diabetes empowerment was associated with not having enough money for food and being on a special diet (P = 0.030). Our qualitative data mirrored quantitative findings that experiencing multiple forms of food stress negatively impacted diabetes psychosocial outcomes and illuminated the cyclical role mental health can play in relationships to food. CONCLUSIONS AND IMPLICATIONS: Our findings highlight that experiencing food stress negatively affects diabetes empowerment and diabetes distress. These findings emphasize the importance of improving community food environments and addressing individual food access for diabetes management and prevention initiatives in American Indian communities.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".