Exploring experiences of food insecurity, stigma, social exclusion, and shame among women in high-income countries: A narrative review
Bibliographic record
Abstract
In Canada, over 4.4 million people experience food insecurity, a serious public health issue characterized by inadequate or insecure access to food due to financial constraints. Globally, women experience disproportionately high rates of food insecurity, which can be a highly stigmatizing experience that is associated with feelings of shame and social isolation. This narrative review explores how and why social beliefs and stigma contribute to social exclusion among women experiencing food insecurity within high-income countries, and how enhancing the capacity for empathetic responses to feelings of shame, and efforts to strengthen women’s resilience to shame, can lead to a reduction in stigma. The thematic analysis of the articles included in this review resulted in four themes: 1) the mechanisms of food insecurity-related social exclusion; 2) charitable responses to food insecurity and stigma, shame, and social exclusion; 3) women’s experiences with food insecurity, stigma, shame, and social exclusion; and 4) empathy and shame resilience. The findings of this review suggest that dominant responses to food insecurity contribute to stigma, shame, and social exclusion among women, and that the inadequacy of existing policy responses to address food insecurity has wide-reaching ramifications on the health and well-being of women and their families.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".