Exploring Food Security and Mental Health Among Street-Involved Canadian 2S/LGBTQI+ Youth: A Review of the Literature
Bibliographic record
Abstract
The purpose of this literature review is to evaluate the extant research addressing food insecurity and mental health among street-involved 2S/LGBTQI+ youth in Canada. Searches were undertaken in academic databases, Google, and Google Scholar for relevant research articles, reports, and grey literature. Our team found nil research specifically addressing food insecurity and the mental health of street-involved 2S/LGBTQI+ youth in Canada. Given that, contextual and contributory factors affecting the mental health and food security of this population are discussed. The available research demonstrates a significant misalignment between the existing support mechanisms and the requirements of this specific population. This underscores the urgent necessity for the establishment of structurally competent, safe, and easily accessible resources. Moreover, there is a clear imperative for additional research endeavors aimed at addressing knowledge deficiencies. These efforts are crucial in empowering dietitians to facilitate enhanced interdisciplinary collaboration, thereby fostering the creation of sustainable, accessible, and appropriate food systems tailored to the needs of this vulnerable demographic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".