The psychological impact of COVID-19 on socially isolated individuals – a systematic review
Bibliographic record
Abstract
Purpose This study aims to assess the various psychological effects of the Coronavirus on those in isolation, the factors that cause these effects during the pandemic, as well as to discuss the recommendations and strategies that can be implemented to help reduce the negative psychological impact. Design/methodology/approach Databases such as MEDLINE (Ovid), excerpta medica dataBASE (Ovid) and cumulative index of nursing and allied health literature (Elton B. Stephens Company), as well as grey literature, were used as a part of the search design to locate articles published on this subject. The search design was formatted in a way that aided in locating articles concerning a variety of mental health effects. Title and abstract screening and full text screening were conducted by two reviewers. The risk of bias assessment was conducted using AMSTAR2. Data extraction was performed by one reviewer and was confirmed by the other. Findings A total of 28 articles were obtained, and of those, 11 were included in the review. From the data that was reviewed, the majority of the isolated individuals felt depressed and anxious. This negative psychological distress contributes to deteriorating health and quality of life in many people. Research limitations/implications Few limitations include limited published research papers pertaining to the subject of the mental health effects of COVID-19 on individuals in social isolation. Therefore, this suggests the need for higher quality studies to be performed on this topic. Originality/value This review provides a unique viewpoint on the effects of COVID-19, allowing for others to have a better understanding of the wide range of psychological impacts the pandemic has brought on.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".