Food Insecurity Among the Adult Population of Colombia Between 2016 and 2019: The Post Peace Agreement Situation
Bibliographic record
Abstract
BACKGROUND: In 2016, a Peace Agreement, explicitly addressing the right to food, was signed, marking the end of more than 50 years of armed conflict and the longest war in the Americas. The expectation was that the years to follow would be marked by rapid social and political change, with the potential to improve food security. OBJECTIVES: (i) Ascertain changes in the prevalence of food insecurity in Colombia between 2016 and 2019; (ii) examine which population subgroups (eg, urban women, rural women, urban men, and rural men) were most vulnerable; and (iii) determine significant individual-level factors predicting food insecurity in these 2 years. METHODS: This study used the Gallup World Poll 2016 and 2019 nationally representative samples of Colombian adults aged 15 and older for the analyses (n ≈ 1000 per year). Food insecurity was measured using the Food Insecurity Experience Scale. Descriptive statistics and logistic regression analyses were conducted using IBM SPSS Complex Samples (version 26). RESULTS: Food insecurity in Colombia increased by 7 percentage points between 2016 and 2019 (from 33% to 40%); women living in rural areas in 2019 reported the highest prevalence (50%). Results from logistic analysis confirm low income, unemployment, and lack of social support were significant predictors of food insecurity in both years. In 2019, gender, low education, and lack of autonomy were also significant predictors. Further research on the determinants of food insecurity is necessary to inform Colombian policies and programs that address food insecurity. The urgency to act is more apparent than ever, given the country's worsening food security profile.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".