Social determinants of food insecurity experienced by Ecuadorian women during the COVID-19 pandemic of Summer 2020: an online survey at the individual level
Bibliographic record
Abstract
The COVID-19 pandemic has amplified concerns about food insecurity, prompting its investigation. An online pilot survey anonymously gathered responses from a non-probabilistic sample of 2058 Ecuadorian women. The Food and Agriculture Organization's Food Insecurity Experience Scale was used to measure moderate or severe food insecurity (MSFI). Data quality was assessed using the Rasch item response theory model; this is a single-parameter logistic model that considers food insecurity severity as a latent trait. The analysis produced MSFI prevalence rates with 90% confidence level margins of error (90%MoE). The highest MSFI was found in women: lacking resources for personal expenses (29.53%, 90%MoE = 3.21) compared to those who had them (12.47, 90%MoE = 1.40); who live in the Amazon region (21.37, 90%MoE = 4.24) versus those living in Highlands (17.66%, 90%MoE = 1.77) or in Coast (13.44%, 90%MoE = 2.40); with three or more children (20.97%, 90%MoE = 4.71) against those without children (12.63%, 90%MoE = 3.57); who experienced income reduction during confinement (18.31%, 90%MoE = 2) compared to those who did not (15.71%, 90%MoE = 1.85); and who are rural (18.13%, 90%MoE = 2.83) versus urban residents (16.63%, 90%MoE = 1.55). This study highlights that the most vulnerable Ecuadorian women experienced the highest food insecurity levels during lockdown, emphasizing the need to consider the intersection between income and sociodemographic factors and their impact on women's food insecurity in future research and policymaking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".