Food insecurity, poverty and lived experience of homelessness: a study of women in Northeastern and Southwestern Ontario
Bibliographic record
Abstract
Understanding the connections between geographical location (Northern vs Southern Ontario) and gender inequalities and food insecurity, poverty, homelessness and health is vital within the current social and political context characterized by restraints in public funding. First, this study describes the experiences of poor and/or homeless women with or without dependents in two mid-size urban communities in Northeastern Ontario (City of Greater Sudbury) and Southwestern Ontario (City of London Ontario) with regard to food insecurity, homelessness, poverty and the perceived impacts on physical and mental health. Second, it identifies the profile of food-insecure women in Northeastern and Southwestern Ontario, as well as the factors associated with their general and mental health perceptions. The study employed a sequential descriptive multi-methods approach to address the objectives. A descriptive, qualitative exploration of food insecurity experiences among poor and/or homeless women in the two regions was conducted. Data were collected through a semi-structured interview with twenty poor and/or homeless women, 10 from each of the two communities. The participants were near homeless or absolutely homeless and all had prior histories of homelessness. The interview data were thematically analyzed. Subsequently, a quantitative secondary data analysis of extracted variables including sociodemographic, health and food insecurity from the Canadian Community Health Survey (CCHS, 2014) was conducted to describe the profile and factors associated with general and mental health perceptions for 408 women in the northeast and southwest of Ontario. The main themes were food and financial hardship, motherhood, resourcefulness and health perceptions. The quantitative findings did not capture the association between health perceptions and place of residency among food-insecure women. The general and mental health perceptions of these women were significantly related to household size, employment, worries about running out of food, inability to afford balanced meals and cutting or skipping meals regardless of where they lived. This study’s findings highlight the intersection of geography, health, gender and vulnerability to food insecurity and show that Northeastern and Southwestern women merit greater attention and support in accessing nutritious food. Such findings are important in shaping gendered public and social policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".