Post-COVID-19 syndrome among healthcare workers in Jordan
Bibliographic record
Abstract
Background: Post-COVID-19 syndrome covers a wide range of new, recurring or ongoing health conditions, which can occur in anyone who has recovered from COVID-19. The condition may affect multiple systems and organs. Aims: To evaluate the frequency and nature of persistent COVID-19 symptoms among healthcare providers in Jordan. Methods: Post-COVID-19 syndrome refers to symptoms extending beyond 4-12 weeks. We conducted a historical cohort study among 140 healthcare staff employed at the National Center for Diabetes, Endocrinology and Genetics, Amman, Jordan. All of them had been infected with COVID-19 virus during March 2020 to February 2022. Data were collected through face-to-face interviews using a structured questionnaire. Results: Some 59.3% of the study population reported more than 1 persisting COVID-19 symptom, and among them 97.5%, 62.6% and 40.9% reported more than 1 COVID-19 symptom at 1-3, 3-6 and 6-12 months, respectively, after the acute phase of the infection. Post-COVID-19 syndrome was more prevalent among females than males (79.5% vs 20.5%) (P = 0.006). The most frequent reported symptom was fatigue. Females scored higher on the Fatigue Assessment Scale than males [23.26, standard deviation (SD) 8.00 vs 17.53, SD 5.40] (P < 0.001). No significant cognitive impairment was detected using the Mini-Mental State Examination and the Montreal Cognitive Assessment scales. Conclusion: More than half (59.3%) of the healthcare workers in our study reported post-COVID-19 syndrome. Further studies are needed to better understand the frequency and severity of the syndrome among different population groups.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".