Awareness of Post-COVID-19 Syndrome Among the General Population of the Kingdom of Saudi Arabia
Bibliographic record
Abstract
Background Post-COVID-19 syndrome (PC19S) is an emerging pathological entity characterized by the development or persistence of a spectrum of symptoms and signs 12 weeks after the original disease. Most COVID-19 patients show a variety of persistent symptoms after recovery that impact their quality of life and professional performance. The prevalence of PC19S is found to be high among many populations hence, the need for knowledge and understanding of its risk factors, symptoms, and the awareness of the population about them to improve the provided health and medical care. Aim This study aims to assess the level of awareness of post-COVID-19 syndrome among the general population of the Kingdom of Saudi Arabia (KSA). Most studies have focused on hospitalized patients and those with severe disease, but PC19S can exist in other categories of COVID-19 patients; hence, the need for total population coverage. Methodology A cross-sectional study was conducted during the period between June 2023 and August 2023 using a structured self-administered online questionnaire. The online questionnaire in addition to the demographic characteristics consists of two main parts, one is about the awareness of the Saudi population of symptoms of PC19S and the other is about awareness of its risk factors. Results The majority of the participants (1558; 72.4%) showed low awareness of PC19S symptoms while only about one quarter (595; 27.6%) showed satisfactory awareness. Also, the awareness of the participants toward risk factors was low, as 1738 (80.7%) of them showed low awareness. We categorized the results into three levels of awareness to simplify and facilitate interpretation. The findings showed that 1380 individuals (64.09%) had low awareness of PC19S, 536 individuals (24.89%) had moderate awareness, and only 237 individuals (11%) had high awareness. The study reported that the highest awareness toward symptoms was of smell disturbances (1206; 56.0%) and the least was of hair loss (506; 23.5%) while among the risk factors, the highest was found toward old age 1326 (61.6%) and the female sex was the lowest 194 (9.0%). Conclusion The study revealed that the majority of the participants demonstrated low awareness of symptoms and risk factors, which needs a continuous effort to raise the population's awareness of this health-threatening condition.
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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 | 0.001 | 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) | 0.002 | 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".