The Impact of “Three Zones” Closed-off Management in Communities on Individuals’ Mental Health and Lifestyle During the COVID-19 Pandemic
Bibliographic record
Abstract
The "three zones" closed-off management in communities is an innovative anti-pandemic measure in China that divides communities into lockdown zones, controlled zones, and precautionary zones to contain the spread of the pandemic and reduce the infection rate. This paper aimed to explore the impact of "three zones" closed-off management in communities on individuals' mental health and lifestyle. Two hundred participants were recruited from Shenzhen city, where a seven-day "three zones" closed-off management was implemented, to complete the online survey made available through a link shared via the Wechat group. The study found that during the lockdown period, individuals’ positive mental health, unhealthy eating behaviors, physical activity, and sleep quality decreased by 8.5%, 5.4%, 22.0%, and 10.2%, respectively, while sedentary time increased by 21.7% markedly. In addition, residents living in controlled zones had poorer mental health but more physical activities than those residing in precautionary zones; residents living in lockdown zones had worse sleep quality and more sedentary time than those living in precautionary zones. These findings are essential to enrich and improve research beyond public health measures during the pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".