Effect of the COVID-19 pandemic on mental health visits in primary care: an interrupted time series analysis from nine INTRePID countries
Bibliographic record
Abstract
Background: (INTRePID) explored primary care visit trends related to mental health conditions in Argentina, Australia, Canada, China, Norway, Peru, Singapore, Sweden, and the USA. Methods: We conducted an interrupted time series analysis in nine countries to examine changes in rates of monthly mental health visits to primary care settings from January 1st, 2018, to December 31st, 2021. Sub-group analysis considered service type (in-person/virtual) and six categories of mental health conditions (anxiety/depression, bipolar/schizophrenia/other psychotic disorders, sleep disorders, dementia, ADHD/eating disorders, and substance use disorder). Findings: Mental health visit rates increased after the onset of the pandemic in most countries. In Argentina, Canada, China, Norway, Peru, and Singapore, this increase was immediate ranged from an incidence rate ratio of 1·118 [95% CI 1.053-1.187] to 2.240 [95% CI 2.057-2.439] when comparing the first month of pandemic with the pre-pandemic trend. Increases in the following months varied across countries. Anxiety/depression was the leading reason for mental health visits in most countries. Virtual visits were reported in Australia, Canada, Norway, Peru, Sweden, and the USA, accounting for up to 40% of the total mental health visits. Interpretation: Findings suggest an overall increase in mental health visits, driven largely by anxiety/depression. During the COVID-19 pandemic, many of the studied countries adopted virtual care in particular for mental health visits. Primary care plays a crucial role in addressing mental ill-health in times of crisis. Funding: Canadian Institutes of Health Research grant #173094 and the Rathlyn Foundation Primary Care EMR Research and Discovery Fund.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".