Disruption of seasonal trends in mental health help-seeking behaviours during the COVID-19 pandemic
Bibliographic record
Abstract
• COVID-19 disrupted seasonal patterns of mental health care utilization in Alberta. • Increase in mental health service utilization in Alberta during the pandemic. • No seasonal differences between sexes in mental health service utilization. • Children displayed different utilization patterns after the pandemic onset. The COVID-19 pandemic significantly impacted mental health globally. This study aims to explore seasonal and pandemic-related patterns in mental health utilization from various sources in Alberta, Canada. We analyzed Alberta's administrative healthcare data to investigate mental health utilization trends. The International Classification of Diseases codes were used to identify mental health disorders, and we examined data by service types and demographic subgroups. The pandemic disrupted the typical seasonal patterns of mental health service use in Alberta, in addition to a notable surge in the initial pandemic months (April to June). Before the pandemic, distinct seasonal patterns were observed, but significant changes occurred after its onset. Notably, children exhibited distinct utilization patterns post-pandemic onset, differentiating them from other age groups. The number of COVID-19 cases did not fully explain these variations, indicating other contributing factors. Physician billing data, which could limit the detail in diagnoses, and the complexity of factors influencing mental health service use pose challenges to a comprehensive analysis. The findings underscore the necessity for tailored mental health strategies that consider age and sex differences and address the evolving needs during and after the pandemic. Future research should delve into the underlying causes of altered service utilization patterns and assess intervention effectiveness, ensuring strategies are responsive and equitable.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".