Mental health consequences of COVID-19 pandemic on adult population: a systematic review
Bibliographic record
Abstract
Purpose The spread of novel coronavirus 2019 (COVID-19) has infected millions of people worldwide. Public health emergencies caused by COVID-19 affect not only people’s physical health but also mental health. This paper aims to summarize recent research findings on the mental health impact of COVID-19 experienced by the general adult population. Design/methodology/approach This paper used a systematic approach and aimed to review the literature on mental health problems faced by general adults during the COVID-19 pandemic. The PubMed database has been selected randomly from the Google Scholar, Cochrane Library, Embase and PubMed databases. Ten journal articles published between January and July 2020 were selected from the PubMed database for the final review. Findings There is growing evidence that COVID-19 may be an objective risk factor for mental distress among the general adult population. More psychological and social support should be provided to protect adult people’s mental health. Practical implications This review will help policymakers develop mental health interventions for the general adult population vulnerable to psychological distress because of COVID-19 pandemic. Originality/value This paper is original and contributes to the existing knowledge that the mental health challenges of COVID-19 are widespread. There is, therefore, a need for more psychological interventions for adults, older adults, in particular, to promote mental health and reduce the distress associated with public health emergencies caused by COVID-19.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".