Chronic diseases and emotional disorders are associated with low perception of quality of life in food insecurity/security
Bibliographic record
Abstract
Understanding individual perception of Quality of Life (QoL) can help combat social and health inequalities. We aimed to identify factors associated with Low Perceived Quality of Life (LPQoL) in 295 adults and older adults with food security and food insecurity, in the city of Natal, Brazil. A cross-sectional study was conducted from June to December 2019, with collection of data on socioeconomic demographic status, lifestyle information, non-communicable diseases (NCDs) and risk factors, emotional disorders, food (in) security and quality of life. To assess food insecurity, the Brazilian Scale of Food Insecurity-EBIA was used, and the WHOQOL-Bref questionnaire was used to assess quality of life. Poisson's Regression was used to verify associations between variables and LPQoL, stratifying the sample into food secure (FS) and food insecure (FI) groups. In the FI group, being overweight, older adult, having no partner, drinking alcoholic beverages twice a week or more, and not having daily availability of water were associated with LPQoL, and in the FS group, having diabetes, monthly family income in the 1st and 2nd tertiles, and never having studied was associated with LPQoL. Reporting emotional disorders and sleeping < 7 h/day were associated with LPQoL in both FI and FS groups. LPQoL was associated with the occurrence of NCDs and risk factors, and emotional disorders, regardless of the food security measure. However, the lack of adequate access to water highlights the social vulnerability of the FI group.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".