Using Peer Support to Strengthen Mental Health During the COVID-19 Pandemic: A Review
Bibliographic record
Abstract
Background: The coronavirus (COVID-19) pandemic has had a significant impact on society's overall mental health. Measures such as mandated lockdowns and physical distancing have contributed to higher levels of anxiety, depression, and other metrics indicating worsening mental health. Peer support, which is peer-to-peer provided social and emotional support, is an underutilized and effective mental health resource that can potentially be used to ameliorate mental health during these times. Objective: This review aims to summarize the toll that this pandemic has had on society's mental health as found in peer-reviewed literature from October 2019 to March 2021, as well as suggest the utility of peer support to address these needs. Methods: References for this review were chosen through searches of PubMed, Web of Science, and Google Scholar for articles published between October 2019 and March 2021 that used the terms: “coronavirus,” “COVID-19,” “mental health,” “anxiety,” “depression,” “isolation,” “mental health resources,” “peer support,” “online mental health resources,” and “healthcare workers.” Articles resulting from these searches and relevant references cited in those articles were reviewed. Articles published in English, French and Italian were included. Results: This pandemic has ubiquitously worsened the mental health of populations across the world. Peer support has been demonstrated to yield generally positive effects on the mental health of a wide variety of recipients, and it can be provided through numerous accessible mediums. Conclusions: Peer support can overall be beneficial for improving mental health during the COVID-19 pandemic and may be an effective tool should similar events arise in the future, although the presence of a few conflicting studies suggests the need for additional research.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".