The Fears of Being Infected by the COVID–19 Virus in Canada: A Look At Germophobes, Crowd-Averse, Fearless and Other Population Segments
Bibliographic record
Abstract
The fear of being infected by the COVID-19 virus is widespread in the Canadian population. This study examined the COVID-19 virus infection fears in a survey sample of 4,200 adult Canadians aged 15 years old and over during the confinement period of June 21-26, 2020 and collected by Statistics Canada. A marketing segmentation analysis was carried out using a roster of 13 perceived health risks items leading to the identification of typical fears and the profiling of five major segments present in the Canadian adult population: "Germophobes" (7%), "Crowd-Averse" (34%), "Fearless" (17%), "Outside "Bubble"-Averse" (18%), and ""Nursing Homes-Averse" (24%). Health risk items included a wide range of preoccupations such as visiting retirement homes, travelling by car or airplane, attending public events, shopping, eating out, seeing doctors and/or participating in sports or gyms. The five population segments were identified using a combination of principal component and k-means cluster statistical analysis. Marketing segmentation is a useful tool for decision makers to categorize population members and, by doing so, facilitate better public campaigns, help design messages, and implement changes that can promote more efficient ways to deal with the various societal consequences of the COVID-19 confinement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".