Learning from “Knocks in Life”: Food Insecurity among Low-Income Lone Senior Women
Bibliographic record
Abstract
Building on earlier quantitative work where we showed that lone senior households reliant on public pensions in Nova Scotia (NS), Canada lacked the necessary funds for a basic nutritious diet, here we present findings from a qualitative study involving in-depth interviews with eight low-income lone senior women living in an urban area of NS. Using a phenomenological inquiry approach, in-depth interviews were used to explore lone senior women's experiences accessing food with limited financial resources. Drawing upon Bronfenbrenner's Ecological Systems Theory, we explored their perceived ability to access a nutritionally adequate and personally acceptable diet, and the barriers and enablers to do so; as well in light of our previous quantitative research, we explored their perceptions related to adequacy of income, essential expenses, and their strategies to manage personal finances. Seven key themes emerged: world view, income adequacy, transportation, health/health problems, community program use, availability of family and friends, and personal food management strategies. World view exerted the largest influence on seniors' personal perception of food security status. The implications of the findings and policy recommendations to reduce the nutritional health inequities among this vulnerable subset of the senior population are considered.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".