Comparing the Pre-COVID-19 Pandemic and During-Pandemic Hospitalizations of Youth Ages 5-24 for Mental Health Disorders in Canada
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic exacerbated mental health issues in children due to the anxiety and fear it propagated worldwide. This study aims to compare the instances of youth hospitalizations for mental health disorders across Canada before and during the COVID-19 pandemic. This paper also compares the differences in rates of youth hospitalizations due to mental health disorders per 100,000 in Western, Eastern, and Northern regions of Canada. Thus, the impact of discrepancies in COVID-19 pandemic restrictions on the prevalence of mental health disorders in youth in different regions of Canada is examined.Methods: The data used in this study was obtained from the Canadian Institute for Health Information (CIHI) database. Data was sorted into the pre-pandemic or during-pandemic period and into Eastern, Western, and Northern provinces and territories. A Wilcoxon Signed Rank Test was conducted to compare the differences in hospitalization rates. A Kruskal-Wallis Test was conducted to compare the differences in pre-pandemic and during-pandemic rates between the geographic regions.Results: Overall, results indicate that the pandemic significantly decreased the prevalence of hospitalizations for mental health disorders in youth in Canada. The results of Wilcoxon Signed Rank Test were statistically significant (p<0.001). The results of the Kruskal-Wallis Test were not statistically significant (p=0.2682).Discussion: Across all regions of Canada, there was a statistically significant decrease in hospitalizations during the pandemic. One possible reason for this decrease is that fear of attending hospitals was exacerbated by the COVID-19 pandemic. Limitations include: data on rates of hospitalizations of youth do not take into account more than 1 hospitalization per child, non-hospitalized cases were not considered, fear of seeking help, lack of extensive coverage, as well as the ongoing nature of the pandemic. The only demographic or social factor covered in this paper is geographic location.Conclusion: Further research is needed to better understand the specific factors contributing to the decrease in hospitalizations for mental health disorders among youth in Canada during the pandemic and to identify effective interventions that can address these issues.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".