Newcomer perceptions of COVID-19 countermeasures in Canada
Bibliographic record
Abstract
Newcomers to Canada have been disproportionally affected by COVID-19, with higher rates of infection and severity of illness. Determinants of higher rates may relate to social and structural inequities that impact newcomers' capacity to follow countermeasures. Our aim was to describe and document factors shaping newcomers' acceptance of COVID-19 countermeasures. Semi-structured qualitative interviews were conducted with individuals living in Canada for <5 years. Participants were asked to discuss their pandemic experiences, and perceptions and acceptance of measures. Five themes were identified: (i) belief in the necessity and efficacy of countermeasures; (ii) negative impact of measures on health/wellbeing; (iii) existing barriers to newcomer settlement exacerbated by pandemic measures; (iv) countermeasure adherence related to immigration status and (v) past experiences shaping countermeasure acceptance. Government should continue to provide messaging regarding the importance of measures for individual and population heath and continue to demonstrate a commitment to the interests of citizens. Importantly, newcomer trust in government should not be taken for granted, as this trust is critical for the acceptance of government interventions now and moving forward. It will be important to ensure that newcomers are given support to overcome challenges to settlement that were intensified 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".