Exploring the nutritional vulnerability of homeless solvent and non-solvent using men in a Canadian urban setting
Bibliographic record
Abstract
This research aimed to explore the nutritional vulnerability of homeless adult men. Using a mixed methods approach, risk factors for chronic illness, food security status, dietary intake adequacy, and how the study participants navigate the food supply system to obtain food were investigated. This study assessed differences in nutrition vulnerability between participants that use solvents and those that do not. The findings reveal that all participants were nutritionally vulnerable. A majority was overweight or obese; nearly all experienced food insecurity; and most did not meet the daily food intake guidelines established by Canada’s Food Guide. Daily efforts by participants to obtain food from charitable meal programs helped to meet physiological needs, as well as social, economic, safety and security needs. Participants using solvents had different nutritional and food experiences than non-solvent users. This was identified by a higher prevalence of severe food insecurity and social exclusion compared to non-solvent using homeless participants. This study provides important information to program planners and policy-makers necessary in order to help meet the food and nutritional needs of adult homeless populations. Findings may be translated into policies and programs aimed at improving accessibility to healthy foods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".