Impact of COVID-19 and other pandemics and epidemics on people with pre-existing mental disorders: a systematic review protocol and suggestions for clinical care
Bibliographic record
Abstract
Introduction The current COVID-19 pandemic has resulted in high rates of infection and death, as well as widespread social disruption and a reduction in access to healthcare services and support. There is growing concern over how the pandemic, as well as measures put in place to curb the pandemic, will impact people with mental disorders. We aim to study the effect of pandemics and epidemics on mental health outcomes for people with premorbid mental disorders. Methods and analysis With our predefined search strategy, we will search five databases for studies reporting on mental health outcomes in people with pre-existing mental disorders during pandemic and epidemic settings. Search dates are planned as follows: 5 May 2020 and 23 July 2020. The following databases will be searched: MEDLINE/PubMed, CINAHL, PsycINFO, MedRxiv and EMBASE. Data will be screened and extracted in duplicate by two independent reviewers. Studies involving non-clinical populations or patients diagnosed with a mental disorder during a pandemic/epidemic will be excluded. We will include data collected from all pandemics and epidemics throughout history, including the present COVID-19 pandemic. If possible, study findings will be combined in meta-analyses, and subgroup analyses will be performed. We hope that this review will shed light on the impact of pandemics and epidemics on those with pre-existing mental disorders. Knowledge generated may inform future intervention studies as well as healthcare policies. Given the potential implications of the current pandemic measures (ie, disruption of healthcare services) on mental health, we will also compile a list of existing mental health resources. Ethics and dissemination No ethical approval is required for this protocol and proposed systematic review as we will only use data from previously published papers that have themselves received ethics clearance and used proper informed consent procedures. Systematic review registration PROSPERO registration number: CRD42020179611.
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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 | 0.109 | 0.115 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.019 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".