Youth mental health care use during the COVID-19 pandemic in Alberta, Canada: an interrupted time series, population-based study
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, youth had rising mental health needs and changes in service accessibility. Our study investigated changes in use of mental health care services for Canadian youth in Alberta before and during the COVID-19 pandemic. We also investigated how youth utilization patterns differed for subgroups based on social factors (i.e., age, gender, socioeconomic status, and geography) previously associated with health care access. METHODS: We used cross-sectional population-based data from Alberta, Canada to understand youth (15-24 year) mental health care use from 2018/19 to 2021/22. We performed interrupted time series design, segmented regression modeling on type of mental health care use (i.e., general physician, psychiatrist, emergency room, and hospitalization) and diagnosis-related use. We also investigated the characteristics of youth who utilized mental health care services and stratified diagnosis-related use patterns by youth subgroups. RESULTS: The proportion of youth using mental health care significantly increased from 15.6% in 2018/19 to 18.8% in 2021/22. Mental health care use showed an immediate drop in April 2020 when the COVID-19 pandemic was declared and public health protections were instituted, followed by a steady rise during the next 2 years. An increase was significant for general physician and psychiatrist visits. Most individual diagnoses included in this study showed significant increasing trends during the pandemic (i.e., anxiety, adjustment, ADHD, schizophrenia, and self-harm), with substance use showing an overall decrease. Mortality rates greatly increased for youth being seen for mental health reasons from 71 per 100,000 youth in 2018/19 to 163 per 100,000 in 2021/22. In addition, there were clear shifts over time in the characteristics of youth using mental health care services. Specifically, there was increased utilization for women/girls compared to men/boys and for youth from wealthier neighborhoods. Increases over time in the utilization of services for self-harm were limited to younger youth (15-16 year). CONCLUSIONS: The study provides evidence of shifts in mental health care use during the COVID-19 pandemic. Findings can be used to plan for ongoing mental health needs of youth, future pandemic responses, and other public health emergencies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".