The mental health benefits of community helping during crisis: Coordinated helping, community identification and sense of unity during the<scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
Communities are vital sources of support during crisis, providing collective contexts for shared identity and solidarity that predict supportive, prosocial responses. The COVID-19 pandemic has presented a global health crisis capable of exerting a heavy toll on the mental health of community members while inducing unwelcome levels of social disconnection. Simultaneously, lockdown restrictions have forced vulnerable community members to depend upon the support of fellow residents. Fortunately, voluntary helping can be beneficial to the well-being of the helper as well as the recipient, offering beneficial collective solutions. Using insights from social identity approaches to volunteering and disaster responses, this study explored whether the opportunity to engage in helping fellow community members may be both unifying and beneficial for those engaging in coordinated community helping. Survey data collected in the UK during June 2020 showed that coordinated community helping predicted the psychological bonding of community members by building a sense of community identification and unity during the pandemic, which predicted increased well-being and reduced depression and anxiety. Implications for the promotion and support of voluntary helping initiatives in the context of longer-term responses to the COVID-19 pandemic are provided. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".