Significantly Increased Public Interest in Sleep Disorder during the COVID-19 Pandemic: An Analysis of Google Trends Data
Bibliographic record
Abstract
Background: The COVID-19 pandemic has had a profound impact on sleep disorders. Previous studies have shown that people's sleep time is delayed. Methods: Analyze the public's interest to the keyword "sleep disorder" during the COVID-19 pandemic from December 2019 to December 2022 using Google Trends. The study also focused on the search trend data of the top three countries with high to low overall search frequency for this keyword. Results: It has been observed that during the peak period of the COVID-19 pandemic, the public's search interest for "sleep disorder" has significantly increased. Analyzing Google Trends from December 2019 to December 2022, the top three countries with high interest in searching for "sleep disorder" are the United States, the Philippines, and Canada. It shows that search interest in the United States is an increasing trend year by year, and the overall trend is relatively stable. The search trend for "sleep disorder" among Filipino netizens fluctuates greatly and is generally on a downward trend. The search trend for "sleep disorder" among Canadian netizens is moderate, and the overall trend is like that of Filipinos. It is worth noting that the Google Trends for "sleep disorder" among Filipino and Canadian netizens has changed from an overall increase to an overall decrease in 2020 as the watershed. This indicates a temporal correlation between the surge in COVID-19 cases and online search for "sleep disorder". Conclusion: It shows that public interest in "sleep disorder" has significantly increased during the COVID-19 pandemic, and there may be a certain correlation between the COVID-19 pandemic and sleep disorders. These are worthy of further exploration by researchers, especially the changes in people's daily routines caused by the COVID-19 pandemic, which in turn affect people's sleep quality.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".