Investigating the impact of quarantine on mental health: insights from the COVID-19 international border surveillance study in Canada
Bibliographic record
Abstract
BACKGROUND: Nations throughout the world are imposing mandatory quarantine on those entering the country. Although such measures may be effective in reducing the importation of COVID-19, the mental health implications remain unclear. AIMS: This study sought to assess mental well-being and factors associated with changes in mental health in individuals subject to mandatory quarantine following travel. METHOD: Travellers arriving at a large, urban international airport completed online questionnaires on arrival and days 7 and 14 of mandated quarantine. Questionnaire items, such as travel history, mental health, attitudes toward COVID-19, and protection behaviours, were drawn from the World Health Organization Survey Tool for COVID-19. RESULTS: There was a clinically significant decline in mental health over the course of quarantine among the 10 965 eligible participants. Poor mental health was reported by 5.1% of participants on arrival and 26% on day 7 of quarantine. Factors associated with a greater decline in mental health were younger age, female gender, negative views toward quarantine measures and engaging in fewer COVID-19 prevention behaviours. For instance, travellers who stated that they rarely wore masks had nearly three times higher odds of developing poor mental health. CONCLUSIONS: Although the widespread use of quarantine may be effective in limiting the spread of COVID-19, the mental health implications are profound and have largely been ignored in policy decisions. Psychiatry has a role to play in contributing to the public policy debate to ensure that all aspects of health and well-being are reflected in decisions to isolate people from others.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".