Knowledge, Beliefs, and Practice toward Seasonal Influenza Vaccine among Adults with Chronic Conditions in Riyadh
Bibliographic record
Abstract
Aims: The present study aimed to assess the knowledge, beliefs, practices, and associated factors (educational level, gender, age, and socioeconomic status) that influence the decision to receive the influenza vaccine and determine whether health-care recommendations affect its acceptance among adults with chronic conditions in Riyadh from 2020 to 2021. Settings and Design: This analytical cross-sectional study was conducted using a convenience sampling technique in Riyadh, Saudi Arabia. Materials and Methods: An electronic questionnaire was distributed through social media, shopping centers, home care unit patients, and outpatient clinics at King Khalid University Hospital in Riyadh, Saudi Arabia. Statistical Analysis Used: Descriptive statistics (frequencies and percentages) were used to describe quantitative and categorical variables. Bivariate statistical analysis was performed using the Chi-square test. Statistical significance was set at P < 0.05. Results: The total number of participants was 518, and 66.4% of those who had received the vaccine ( n = 211) had high knowledge. Of the 210 male participants, 68.1% had received the vaccine, and of the 308 female participants, 56.8% had received the vaccine. The main motives for vaccine uptake were worry about contracting influenza (48.1%) and health-care professionals’ recommendations (44.6%). Conclusions: Influenza vaccination in adults with chronic diseases in Riyadh is suboptimal. Physician recommendations are the main motivator, whereas the fear of side effects is the main barrier. Educational campaigns should be conducted to promote knowledge and address misconceptions, fears, and false beliefs about the influenza vaccine, along with increasing physicians’ recommendations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".