Study of Factors Associated with the Rate of COVID-19 Infection
Bibliographic record
Abstract
Due to the ongoing COVID-19 pandemic, problems continue to arise in Thailand and many other countries worldwide. Therefore, each country has made various efforts to find solutions to these issues, some of which have been successful while others have not. In order to effectively address the spread of the disease, it is crucial to understand the variables that are related to the infection. The objective of this research was to study the correlation between various factors and the transmission of COVID-19. The sample group consisted of patients with respiratory system-related illnesses who received treatment at Phonthong Hospital, Phonthong District, Roi Et Province, in March 2022. The total number of participants was 597. The data collection tools included a questionnaire that met quality criteria and statistical analysis tools such as frequencies, percentages, and chi-square. The research findings revealed statistically significant correlations at the .05 level between the following factors and COVID-19 infection: age, vaccination status, and the number of vaccine doses received. On the other hand, factors such as gender, occupation, and underlying medical conditions showed no correlation with the infection.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".