Youth mental health care use during COVID-19: Evidence from administrative data in Alberta, Canada
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic affected health care service delivery and accessibility. Our study examined how Canadian youth used mental health services before and during the COVID-19 pandemic, and whether youth access and use patterns differed for subgroups based on social determinants previously associated with health care access. Methods Cross-sectional population-based administrative health care data from the province of Alberta in Canada was used to examine youth (ages 15-24) mental health care use from 2018/19 to 2021/22. Data was analyzed using an interrupted time series design and segmented regression modeling on type of mental health care use. Outcome measures were mental health care use for: general physician visits, psychiatrist visits, emergency department, hospitalization, and different mental health diagnoses. We stratified by factors related to social determinants, including gender and socioeconomic status. Results The analysis indicates an overall increase in the proportion of youth that used mental health care from 2018/19 to 2021/22 (14.4% vs 17.5%, respectively). There was an immediate drop in use with the onset of the COVID-19 pandemic in Canada, followed by a steady rise during the pandemic years, which was largely due to an increase in general physician visits. This rise was seen for most mental health diagnoses of study (i.e., anxiety, adjustment, attention-deficit/hyperactivity disorder, schizophrenia, and self-harm disorders). Service use increased for young women/girls compared to young men/boys and for youth from wealthier neighborhoods. Conclusions This study provides evidence to support health services planning and delivery to respond to the mental health needs of youth and to prepare for future public health emergencies. Key messages • Our study findings indicate changes in health care use for mental health among Canadian youth (ages 15-24) during the COVID-19 pandemic, with use patterns differing for youth subgroups. • Administrative health data can provide evidence to support health services planning and delivery to respond to the mental health needs of youth and to prepare for future public health emergencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".