O EFEITO DA QUALIDADE DE VIDA PARA A SAÚDE MENTAL DE GESTANTES EM SITUAÇÃO DE INSEGURANÇA ALIMENTAR
Bibliographic record
Abstract
Mental suffering is associated with inadequate access to food, and quality of life may affect this relationship. The aim of this study was to analyze the influence of quality of life on the relationship between food insecurity and anxiety among pregnant women residing in João Pessoa, Paraíba. This is a cross-sectional study conducted with a sample of 271 pregnant women recruited at Family Health Units during routine prenatal consultations. In addition to sociodemographic characteristics, family food insecurity was assessed using the Brazilian Food Insecurity Scale, anxiety risk was assessed using the State-Trait Anxiety Inventory, and quality of life was measured using the WHOQOL-brief instrument from the World Health Organization. The associations between the study variables were tested using the chi-square test, binary logistic regression, network analysis, and mediation analysis. Food insecurity affected 45.4% of the study participants, and 56.1% were classified as at risk of anxiety. Food insecurity increased the risk of anxiety by 2.2 times (OR: 2.22; 95% CI: 1.29 – 3.81). Network analysis showed the complex relationship between the study variables, which, when included in a multiple mediation model, identified that the social relations domain of quality of life had a mediation effect of 25.2% (β = -0.77) on the direct relationship between food insecurity and anxiety risk. The results highlighted that social support networks have the potential to reduce the negative impact of food insecurity on the anxiety risk of pregnant women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".