The Relationship of Food Insecurity and Mental Health in Single-Parent Households: A Literature Review
Bibliographic record
Abstract
Introduction: Food insecurity is a prominent concern worldwide. While food insecurity is recognized as an extensive physical health concern globally, it also has impacts on our mental well-being, contributing to elevated rates of depression and stress, among other symptoms of psychological distress. This relationship between food insecurity and poor mental health is heavily supported; however, previous research on this topic has focused on nuclear families as opposed to single-parent family structures. This review aims to comprehensively examine food insecurity and mental health in single-parent households to better understand the role that family structure may play in this relationship. Methods: A literature search was conducted using three terms stipulated for study inclusion: “mental health”, “food insecurity”, and “single parent”. Primary studies were identified with no restrictions on study type and type of data (qualitative or quantitative) for a more comprehensive review. Results: All of the studies included in this review support a relation between food insecurity and mental health in single-parent households, with individuals in this demographic experiencing higher rates of both compared to the general population. This relation was observed in people from a variety of developed countries, suggesting some degree of generalizability. Discussion: It can be conclusively established that being in a single-parent household increases the risk of food insecurity, contributing to poor mental health within this population. There are many gaps in the literature on single-parent households to explore further, including the experience of single fathers and how this relation appears in developing countries as well as a need to explore a wider range of mental health conditions resulting from food insecurity. Conclusion: Food insecurity is a significant concern in single-parent households. This increases the risk of poor mental health in these households, seen in higher rates of depression, among other conditions. Public health institutions should consequently consider this demographic when working towards new programs or initiatives aimed at addressing food insecurity and mental health problems.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".