A scoping review of international policy responses to mental health recovery during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has affected people's physical and mental health. Quarantine and other lockdown measures have altered people's daily lives; levels of anxiety, depression, substance use, self-harm and suicide ideation have increased. This commentary assesses how international governments, agencies and organisations are responding to the challenge of the mental health impact of COVID-19 with the aim of informing the ongoing policy and service responses needed in the immediate and longer term. It identifies some of the key themes emerging from the literature, recognises at-risk populations and highlights opportunities for innovation within mental health services, focusing on the published academic literature, international health ministry websites and other relevant international organisations beyond the United Kingdom and Ireland. COVID-19 has challenged, and may have permanently changed, mental health services. It has highlighted and exacerbated pre-existing pressures and inequities. Many decision-makers consider this an opportunity to transform mental health care, and tackling the social determinants of mental health and engaging in prevention will be a necessary part of such transformation. Better data collection, modelling and sharing will enhance policy and service development. The crisis provides opportunities to build on positive innovations: the adaptability and flexibility of community-based care; drawing on lived experience in the design, development and monitoring of services; interagency collaboration; accelerating digital healthcare; and connecting physical and mental health.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".