Gender matters: Exploring the mental health of youth experiencing homelessness, a qualitative study
Bibliographic record
Abstract
It is well known that gender shapes mental health experiences broadly. Gender adds an additional layer to the already complex experience of being a male, female, or transgender young person who is experiencing homelessness. Yet, research in this area is limited. The purpose of this qualitative, interpretive description study was to understand how gender shapes the mental health of youth who are experiencing homelessness in the Niagara Region of Ontario, Canada. Eleven young people between the ages of 16 and 24 were recruited from a youth shelter and participated in a semi-structured interview. Interviews were audio-recorded and transcribed so that data could be analyzed using thematic analysis and Gender Based Analysis Plus. Findings revealed four contextual factors that appear to influence a young person’s mental health while homeless, but that are experienced differently depending on one’s gender identity. These factors include (1) housing acquisition is challenging, (2) appearances are meaningful, (3) cleanliness and hygiene are expected, and (4) utilizing mental health resources is complicated. Additionally, the many strengths that the youth identified and demonstrated in navigating their circumstances are highlighted in our results. These strengths include (1) exhibiting resilience, (2) expressing the ability to survive, (3) imagining a world that is better, (4) articulating their needs, and (5) drawing on their social connections. The gendered lens that guides this study provides a challenge to the homogenous way that young people experiencing homelessness are often portrayed within the literature. The experiences of young people who live with homelessness cannot simply be addressed within the siloed categories of gender, homelessness, and age. Ensuring that interventions are tailored to meet young people’s specific gendered needs is both a matter of human rights and health equity. Practical implications for service providers are discussed.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".