Association between food security and sociodemographic factors: cross-sectional study on a POAPMC beneficiaries
Bibliographic record
Abstract
Introduction: Food insecurity is a complex problem with several factors such as sociodemography. In addition, it is associated with harmful effects on health such as inadequate food intake and the consequent emergence of chronic diseases, mental health problems, among others. Objectives: Determine the association between sociodemographic factors and the food security status in households supported by the Operational Programme to Support Most Deprived People (POAPMC – Programa Operacional de Apoio às Pessoas Mais Carenciadas). Material and methods: A cross-sectional, observational, quantitative, and analytical study, carried out at Santa Casa da Misericórdia in Vieira do Minho with a sample of 71 individuals who are POAPMC beneficiaries. It was applied a questionnaire on sociodemographic factors and a food insecurity scale. The analytical study involved association tests, namely the Pearson Chi-square test and the Spearman test. Results: Food insecurity was found in 74,6% of respondents, 14,1% in the severe level, 22,5% in the moderate level, and 38,0% in the low level. According to the Spearman test, the sociodemographic variable that showed an association with the food security status was the level of education completed (Rho = -0,23, p = 0,050). This correlation is negative, that is, as the level of education increases, the level of food insecurity decreases. Conclusions: The level of education seems to be associated with the food security status of POAPMC beneficiaries. Thus, the results of this study show that the educational level of each beneficiary must be considered in the creation of new public policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 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".