Dental care during the COVID-19 Pandemic: An Arabic tweets analysis (Preprint)
Bibliographic record
Abstract
Background: Twitter is a powerful platform which could be used to improvise the demand and supply of dental services during a pandemic. Objective:The aim of this study was to examine the nature and dissemination of COVID-19 information related to dentistry on Twitter platform Arabic database.Methods: One hundred and fifty independent searches with a combination of keywords for both COVID-19 and dentistry from a preselected Arabic keyword were carried out for the period from the 2nd of March to the 6th of July 2020.Tweets were filtered to remove duplicate and unrelated tweets.The suitable tweets were 1,150.After calibration, two examiners coded the tweets following two main themes: COVID-19 and oral health-related information.Tweets were then compared with COVID-19 daily events in the Arab counties as reported by the World Health Organization (WHO).Descriptive analysis was performed to present the overview of the findings using Microsoft Excel.Results: There was no obvious association between time distribution of the tweets to the distribution of new COVID-19 cases and deaths during the period from March 2, 2020 to July 6, 2020.The most retweeted information was the help with urgent consultation or emergency dental treatment during COVID-19 tweeted by a dentist.There were 673 retweets and 1116 likes of this tweet.The most common tweets related to oral health was needs of dental treatment (n=462, 39.5%) of which, toothaches or wisdom tooth problems constituted 48% of the related tweets. Conclusions:Twitter is a platform reflecting the public interest and concerns, based on the finding tweets tend to increase with major events and news and thus help navigate the proper action needed to address public concern.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".