Online symptoms self-assessment during COVID-19 pandemic: an analysis of a COVID-19 portal responses from Canada
Bibliographic record
Abstract
COVID-19 case was first identified in Canada on January 25, 2020, on a Toronto resident who had travelled to Wuhan China, and not long after, the WHO declared the viral infection a pandemic. Ontario health West created an online self-assessment portal that allowed individuals in the health region and adjourning areas to report any COVID related symptoms. The purpose of this study was to evaluate the utility and usefulness of the Ontario Heath West online COVID-19 self-assessment portal. Record level data obtained from the Ontario Health West self-assessment portal was analyzed. Descriptive statistics using charts and graphs were used to characterize the distribution of responses to the portal. In-depth analysis using correlation, lead-lag analysis, and trend comparison with actual Government of Ontario COVID-19 cases for the region were also conducted. A total of 34,144 distinct responses were recorded on the portal between April 10 and July 29, 2020, with 1,250 (3.7%) responding positively to one of the emergency symptoms questions. Trend analysis showed a peak portal response in May 2020 with a smaller rise subsequently in July 2020, coinciding with the actual COVID-19 peak in the region. The five most reported symptoms on the portal were sore throat (17.2%), headache (12.9%), fatigue (12.3%), digestive problems (12.2%) and cough (9.1%). For four sub-regions, the trend of self-report on the portal positively lagged actual Public Health Ontario reported COVID-19 cases, while for one sub-region, the trend positively led the actual Public Health Ontario reported COVID-19 cases for the area. We found correlation between online COVID-19 self- assessment data and the confirmed COVID-19 cases in the Southwestern region of Ontario. Trends in the COVID-19 associated emergency symptoms reported on the portal also tracked confirmed COVID-19 cases in the community. Peak response to the portal coincided with the peak volume of confirmed cases in Ontario during the first wave of COVID-19 pandemic in Canada, suggesting some consistency between the experiences of portal users and patterns of COVID-19 illness in the community. The portal was a useful tool at the person-level because it provided guidance to individuals about how to access appropriate health services according to the symptoms that they reported and connected them with primary care, reducing unnecessary visit to health facilities for COVID-19 related care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".