Stress and Hypertension Among Primary School Teachers In Enugu North Senatorial Zone Enugu State, Nigeria
Bibliographic record
Abstract
security ("in the last 30 days, I worried that the food I was able to eat would hurt my health and well-being").Participants also completed a food frequency questionnaire via VioScreen.The analytic sample consisted of 619 individuals from RI and 628 from CT. Results describe the baseline prevalence of food and nutrition security and test whether these varied by quintiles of the Healthy Eating Index (HEI) score.The statistical significance of differences at baseline was assessed using Pearson's chi-squared test.Results: Characteristics of respondents in RI and CT were similar.Mean age was 35.3 (SD11.7),92% were female, and 43% of respondents reported that they were Hispanic, followed by non-Hispanic White (31%), and non-Hispanic Black (16%).Lack of money was the primary barrier to accessing food (40%), followed by few affordable grocery stores (18%).The total average HEI score was 63.9 and 58% and 30% of the sample were food and nutrition insecure, respectively.Food insecurity varied significantly across quintiles of HEI (p0.02), with fewer adults reporting food insecurity (49%) in the highest quintile vs. lowest HEI quintile (60%).Nutrition insecurity did not vary significantly across HEI quintiles (p0.72).Conclusions: The prevalence of food insecurity was higher than the national average, and higher food insecurity was associated with worse diet quality, underscoring the need for enhanced nutrition benefits among SNAP participants.Future work will evaluate the impact of the nutrition incentive program on food and nutrition security.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".