Access to Mental Health and Substance Use Resources for 2SLGBTQ+ Youth during the COVID-19 Pandemic
Bibliographic record
Abstract
Previous research has established that gender and sexual minority (2SLGBTQ+) youth experience worse mental health and substance use outcomes than their heterosexual and cisgender counterparts. Research suggests that mental health and substance use concerns have been exacerbated by the COVID-19 pandemic. The current study used self-reported online survey responses from 1404 Canadian 2SLGBTQ+ youth which included, but were not limited to, questions regarding previous mental health experiences, diagnoses, and substance use. Additional questions assessed whether participants had expressed a need for mental health and/or substance use resources since the beginning of the COVID-19 pandemic (March 2020) and whether they had experienced barriers when accessing this care. Bivariate and multinomial logistic regression analyses were conducted to determine associations between variables and expressing a need for resources as well as experiencing barriers to accessing these resources. Bivariate analyses revealed multiple sociodemographic, mental health, and substance use variables significantly associated with both expressing a need for and experiencing barriers to care. Multinomial regression analysis revealed gender identity, sexual orientation, ethnicity, and level of educational attainment to be significantly correlated with both cases. This study supports growing research on the mental health-related harms that have been experienced during the COVID-19 pandemic and could be used to inform tailored intervention plans for the 2SLGBTQ+ youth population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".