Food insufficiency is associated with depression among street-involved youth in a Canadian setting
Bibliographic record
Abstract
OBJECTIVE: Food insufficiency, defined by the experience of hunger, is known to be prevalent and a source of health-related harm among-street involved youth, but little is known about its relationship with depression in this population. Therefore, we sought to assess the association between food insufficiency and symptoms of depression among a cohort of street-involved youth. DESIGN: Multivariable logistic regression was used to assess the relationship between food insufficiency, defined as being hungry but not having enough money to buy food, and depression as measured by the Center for Epidemiological Studies Depression (CES-D) scale. SETTING: Data from April 2006 to November 2013 were derived from the At-Risk Youth Study (ARYS), a prospective cohort of street-involved youth who use illicit drugs in Vancouver, Canada. SUBJECTS: There were 1066 street-involved youth enrolled in the study, including 340 (31·9 %) females. RESULTS: Of 1066 youth enrolled in the study, 724 (67·9 %) reported some food insufficiency and 565 (53·0 %) met criteria for depression. Compared with youth who did not report food insufficiency, those who reported often experiencing food insufficiency had a higher likelihood of reporting depression (adjusted OR=2·52; 95 % CI 1·74, 3·67), as did those who reported sometimes experiencing food insufficiency (adjusted OR=1·99; 95 % CI 1·47, 2·70). CONCLUSIONS: Food insufficiency was prevalent and associated in a dose-dependent trend with symptoms of depression among street-involved youth in our setting. Findings highlight the need to address the nutritional and mental health needs of youth and identify pathways by which food insufficiency may contribute to depression among vulnerable populations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".