Sociodemographic factors associated with Covid-19 in Canada
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease or Covid-19 has caused more than 30 million documented infections and 1 million deaths worldwide as of Oct 2020. It was shown that several sociodemographic factors play a significant role in shaping the Covid-19 outcome and associated death rates across the globe. Thus the present study aims to study the sociodemographic parameters associated with the Covid-19 cases in Canada. MATERIAL AND METHODS: In this retrospective study, the data was collected from the Official data repository present in Canada. The patients' data were evaluated and sociodemographic parameters were checked and recorded. After the data was recorded they are categorized based on the different states and statistical analysis was done. RESULT:The present study reported that in Canada total cases as reported in the repository are 1,253,519 cases. This result indicates that maximum of the patients suffering from Covid-19 belonged to the younger age category. Compared to the males, females were more to suffer from Covid-19. Most of the patients who required hospitalization were in the 80+ year age group (28.5%). Only 1.7% of patients in the age group below 19 years are required to be hospitalized. The regional data variation showed that in Alberta female patients were more in all the age groups compared with the male patients. Saskatchewan also reported a higher number of death cases in older people. In Manitoba, in the younger age category (0-29 years) less number of female patients suffered Covid-19. Interestingly, this number reversed as the age group increased. In Ontario, 72.1% of people reported being admitted to ICU and required a ventilator. In British Columbia, the gender distribution showed no such difference among all the Covid-19 positive cases. In Quebec among the covid-19 positive cases, 47.2% were male and 52.8% were females. CONCLUSION: Age is a significant predictor of Covid-19 mortality and patients from both genders aged more than 75 years and more need to provide more care and increased medical supervision to decrease the Covid-19 casualty.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".