Social Acceptability of Quarantine in Public Health Emergencies: A Systematic Review
Bibliographic record
Abstract
Abstract Background: Quarantine and isolation is an effective method of controlling outbreaks of emerging and re-emerging infectious diseases. However, the effectiveness of these interventions depends on a high compliance rate, which is often compromised by multiple reasons to break quarantine or refuse isolation. In this systematic review, we highlight public attitudes and reactions towards quarantine including factors that may hinder quarantine measures during public health epidemics, public preferences for using quarantine during epidemics or infectious disease outbreaks and key considerations for the use of quarantine in public health epidemics.Methods: We searched five databases for publications on quarantine and isolation, and screened for empirical studies on social acceptability.Results: We found 17 articles that met the inclusion criteria. A review of the articles showed some factors could impede compliance with quarantine and isolation. These include the feelings of guilt and social distress, concern about loss of income, and self and social stigma and/or discrimination. On the other hand, compliance with quarantine and isolation was positively associated with perceptions of being a civic duty or fears of infecting loved ones. The articles concluded that, quarantine compliance can be enhanced through assurance of income and promoting safe interaction with loved ones during isolation/quarantine. Conclusions: This review provides public health experts, emergency planners, and policy makers with key considerations to improve public compliance with isolation and quarantine measures during public health emergencies.
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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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".