A growing need for youth mental health services in Canada: examining trends in youth mental health from 2011 to 2018
Bibliographic record
Abstract
AIMS: The mental health of youth is continually changing and requires reliable monitoring to ensure that adequate social and economic resources are allocated. This study assessed trends in mental health among Canadian youth, 12-24 years old. Specifically, we examined the prevalence of poor/fair perceived mental health, diagnosis of mood and anxiety disorders, suicidality, perceived stress and sleep problems, substance use, and mental health consultations. METHODS: Data were collected from eight cycles of the annual Canadian Community Health Survey (2011-2018). Prevalence of mental health outcomes was calculated from each survey, and meta-regression was used to assess trends over time. In the absence of a significant trend over time, the eight cycles were pooled together using meta-analysis techniques to gain precision. Trends in prevalence were assessed for the overall sample of youth (12-24 years) and separately for male and female adolescents (12-18 years) and young adults (19-24 years). RESULTS: The prevalence of poor/fair perceived mental health, diagnosed mood and anxiety disorders, and past-year mental health consultations increased from 2011 to 2018, most strongly among young adult females. Past-year suicidality increased among young adult females but did not change for other age and sex groups. Notably, the prevalence of binge drinking decreased by 2.4% per year for young adult males, 1.0% for young adult females and 0.7% per year for adolescent males, while staying relatively stable for adolescent females. Prevalence of cannabis use declined among adolescents before legalisation (2011-2017); however, this trend did not persist in 2018. Instead, the 2018 prevalence was 5.6% higher than the 2017 prevalence (16.3 v. 10.7%). The combined prevalence of other illicit drug use was stable at 4.6%; however, cocaine use and hallucinogens increased by approximately 0.2% per year. CONCLUSIONS: Our findings highlight a growing need for youth mental health services, as indicated by a rise in the prevalence of diagnosed mood and anxiety disorders and past-year mental health consultations. The reason for these observed increases is less apparent - it may represent a true rise in the prevalence of mental illness, or be an artefact of change in diagnostic practices, mental health literacy or diminishing stigma. Nonetheless, the findings indicate a need for the health care system to respond to the rising demand for mental health services among youth.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".