Challenges facing mental health systems arising from the COVID-19 pandemic: Evidence from 14 European and North American countries
Bibliographic record
Abstract
We assessed challenges that the COVID-19 pandemic presented for mental health systems and the responses to these challenges in 14 countries in Europe and North America. Experts from each country filled out a structured questionnaire with closed- and open-ended questions between January and June 2021. We conducted thematic analysis to investigate the qualitative responses to open-ended questions, and we summarized the responses to closed-ended survey items on changes in telemental health policies and regulations. Findings revealed that many countries grappled with the rising demand for mental health services against a backdrop of mental health provider shortages and challenges responding to workforce stress and burnout. All countries in our sample implemented new policies or initiatives to strengthen mental health service delivery - with more than two-thirds investing to bolster their specialized mental health care sector. There was a universal shift to telehealth to deliver a larger portion of mental health services in all 14 countries, which was facilitated by changes in national regulations and policies; 11 of the 14 participating countries relaxed regulations and 10 of 14 countries made changes to reimbursement policies to facilitate telemental health care. These findings provide a first step to assess the long-term challenges and re-organizational effect of the COVID-19 pandemic on mental health systems in Europe and North America.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".