Online Public Interest in Cancer During the COVID-19 Pandemic
Bibliographic record
Abstract
PURPOSE Health care priorities of individuals may change during a pandemic, which may, in turn, affect health services utilization. We examined Canadians' online relative search interest in five common solid tumors (breast, colon, lung, prostate, and thyroid) during the COVID-19 pandemic to that observed in the same months in the prior 5 years. METHODS We conducted a cross-sectional retrospective study using Google Trends aggregate anonymous online search data from Canada. We compared the respective relative search volumes for breast, colon, lung, prostate, and thyroid cancers for the months March-November 2020 with the mean for the same months in 2015-2019. Welch's two-sample t tests were performed and the raw P values were then adjusted using Benjamini-Hochberg procedure to correct for multiple comparisons. The level of statistical significance was defined by choosing false discovery rate at .05 for the primary analysis. RESULTS We observed temporary statistically significant reductions in Canadians' relative search volumes for various cancers, largely early in the pandemic, in the spring of 2020. Specifically, significant reductions (after adjustment for multiple comparisons) were observed for breast cancer in April, May, and October 2020; colon cancer in March and April of 2020; lung cancer in April and September 2020; and prostate cancer in April and May 2020. Thyroid cancer relative search volumes were not significantly different from those observed prior to the pandemic. CONCLUSION Although Canadians' online interest in various cancers temporarily waned early in the COVID-19 pandemic, recent relative search volumes for various cancers are largely not significantly different from prior to the pandemic.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".