The Pathways study: a cohort study of new food-aid users in rural, semi urban, and urban areas of Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: While considerable research has been conducted on household food insecurity (HFI), little research has examined the effects of food donation programs on users' living conditions. The Pathways study was established to investigate the long-term effects of food donation programs on food insecurity as well as other critical outcomes, such as diet, health, and social support. Herein, we describe the design of the Pathways Study and the participants' characteristics at baseline. METHODS: The Pathways study is a prospective cohort study of 1001 food-aid users in Quebec (Canada). We recruited newly registered users of food donation programs from 106 community-based food-aid organizations that partnered with the study. Baseline data were collected through face-to-face interviews from September 2018 to January 2020, with planned follow-up interviews at 12 and 24 months after enrollment. Household food insecurity, diet, food competencies, food shopping behaviors, perceived food environment, health status, social support and isolation, sociodemographic characteristics, housing conditions, negative life events, and the impacts of COVID-19 were assessed with validated questionnaires. RESULTS: The cohort included 1001 participants living in rural (n = 181), semi-urban (n = 250), and urban areas (n = 570). Overall, household food insecurity was reported as severe among 46.2% and moderate in 36.9% of participants. Severe household food insecurity was more prevalent in rural (51.4%) and urban (47.8%) areas compared to semi-urban (39%) areas. Overall, 76.1% of participants reported an annual income below C$20,000. Half (52%) had low education levels (high school or lower), 22.0% lived in single-parent households, and 52.1% lived alone. Most (62.9%) experienced at least one major financial crisis in the preceding year. CONCLUSIONS: Results show that newly registered users of food donation programs often have low-income and severe food insecurity, with major differences across geographical locations. The Pathways study is the first study designed to follow, over a 2-year period, a cohort of newly registered users of food donation programs and to quantify their trajectories of service use. Findings from the Pathways study might help adapt the community response to the strategies used by food-insecure households to feed themselves.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".