Healthy eating in persons with serious mental illnesses: Understanding and barriers.
Bibliographic record
Abstract
OBJECTIVE: To explore the understanding of a healthy diet and the barriers to healthy eating in persons with serious mental illnesses. METHODS: In-depth semi-structured qualitative interviews about health behaviors were conducted in 31 individuals with serious mental illnesses. Participants were recruited from a mental health center in Chicago, Illinois, and ranged in age from 30 to 61 years old. RESULTS: Most participants described healthy eating as consuming fruits and vegetables, using low fat cooking methods, and limiting sweets, sodas, fast food, and/or junk food. Internal barriers to nutritional change included negative perceptions of healthy eating, the decreased taste and satiation of healthy foods, difficulty changing familiar eating habits, eating for comfort, and the prioritization of mental health. External barriers were the reduced availability and inconvenience of healthy foods, social pressures, and psychiatric medication side effects. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: This study revealed several modifiable barriers to healthy eating. Interventions that addressed these could aid in improving the diet and lowering the risk of cardiovascular disease in this population. Recommendations are to provide healthy eating education that is individualized, emphasizes the health consequences of poor eating, and provides opportunities to prepare and taste healthy foods. Family and friends should be included in all educational efforts. At community mental health centers and group homes, only healthy foods should be offered. Lastly, practitioners should encourage eating a healthy diet, inquire about eating in response to emotions, and explore the impact of psychiatric medications on eating behaviors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".