Factors Influencing Compliance with Quarantine in Toronto During the 2003 SARS Outbreak
Bibliographic record
Abstract
The purpose of this study was to cull lessons from Toronto's experiences with large-scale quarantine during the outbreak of Severe Acute Respiratory Syndrome in early 2003. We focused on issues that affected the population's willingness to comply with quarantine. Information was acquired from interviews, telephone polling, and focus groups. Issues of quarantine legitimacy, criteria for quarantine, and the need to allow some quarantined healthcare workers to leave their homes to go to work were identified. Also important was the need to answer questions from people entering quarantine about the continuation of their wages, salaries, and other forms of income while they were not working, and about the means by which they would be supplied with groceries and other services necessary for daily living. The threat of enforcement had less effect on compliance than did the credibility of compliance-monitoring. Fighting boredom and other psychological stresses of quarantine, muting the forces of stigma against those in quarantine, and crafting and delivering effective and believable communications to a population of mixed cultures and languages also were critical. The need for officials to develop consistent quarantine policies, procedures, and public messages across jurisdictional boundaries was paramount.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".