“I feel like I’m in a revolving door, and COVID has made it spin a lot faster”: The impact of the COVID-19 pandemic on youth experiencing homelessness in Toronto, Canada
Bibliographic record
Abstract
PURPOSE: Research has shown that youth experiencing homelessness (YEH) face barriers to social inclusion and are at risk for poor mental health. With the COVID-19 pandemic threatening the health, wellbeing, and economic circumstances of people around the world, this study aims to assess the impacts of the pandemic on YEH in Toronto, Ontario, as well as to identify recommendations for future waves of COVID-19. METHODS: Semi-structured interviews were conducted with YEH (ages 16-24, n = 45) and staff who work in one of four downtown emergency shelters for youth (n = 31) in Toronto, Ontario. RESULTS: YEH experienced both structural changes and psychosocial impacts resulting from the pandemic. Structural changes included a reduction in services, barriers to employment and housing, and changes to routines. Psychosocial outcomes included isolation, worsened mental health, and increased substance use. Impacts were magnified and distinct for subpopulations of youth, including for youth that identified as Black, 2SLGBTQ+, or those new to Canada. CONCLUSIONS: The COVID-19 pandemic increased distress among YEH while also limiting access to services. There is therefore a need to balance health and safety with continued access to in-person services, and to shift the response to youth homelessness to focus on prevention, housing, and equitable supports for subpopulations of youth.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".