The Nexus Between Family Food Insecurity and Mental Health of its Members: A Review
Bibliographic record
Abstract
Food is one of the primary sources of sustainability for families. A family withinconsistent access to adequate, safe, and nutritious food is considered foodinsecure, which can negatively impact their mental well-being. Studies haveshown a significant increase in the number of food-insecure Nigerians, risingfrom 66.2 million in Q1 2023 to 100 million in Q1 2024. This paper reviewedexisting literature on family food insecurity, its causes, and its consequencesfor the mental health and well-being of families, utilizing Google Scholar asthe search tool. The literature established that family food insecurity issignificantly linked to anxiety, emotional distress, depression, and othermental health issues among family members. Poor mental health can hinderthe ability to access safe and nutritious foods; thus, as access to adequate foodand nutrition decreases, family members experience increased anxiety,emotional distress, and depression. Additionally, worsening mental healthcan lead to decreased productivity among family members, furtherintensifying food insecurity, thus creating a cyclical relationship between foodinsecurity and mental health. Policy and public health interventions mustaddress the intertwined issues of food insecurity and mental hea lth.Therefore, there is a call to action for food security to be included as a criticalcomponent of primary healthcare that every family should have access to.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".