Meeting the service needs of youth with and without a self-reported mental health diagnosis during COVID-19.
Bibliographic record
Abstract
Background: The COVID-19 pandemic catalyzed major changes in how youth mental health (MH) services are delivered. Understanding youth's MH, awareness and use of services since the pandemic, and differences between youth with and without a MH diagnosis, can help us optimize MH services during the pandemic and beyond. Objectives: We investigated youth's MH and service use one year into the pandemic and explored differences between those with and without a self-reported MH diagnosis. Methods: In February 2021, we administered a web-based survey to youth, 12-25 years, in Ontario. Data from 1373 out of 1497 (91.72%) participants were analyzed. We assessed differences in MH and service use between those with (N=623, 45.38%) and without (N=750, 54.62%) a self-reported MH diagnosis. Logistic regressions were used to explore MH diagnosis as a predictor of service use while controlling for confounders. Results: 86.73% of participants reported worse MH since COVID-19, with no between-group differences. Participants with a MH diagnosis had higher rates of MH problems, service awareness and use, compared to those without a diagnosis. MH diagnosis was the strongest predictor of service use. Gender and affordability of basic needs also independently predicted use of distinct services. Conclusion: Various services are required to mitigate the negative effects of the pandemic on youth MH and meet their service needs. Whether youth have a MH diagnosis may be important to understanding what services they are aware of and use. Sustaining pandemic-related service changes require increasing youth's awareness of digital interventions and overcoming other barriers to care.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".