Prevalence, Risk Factors, Disease-Related Knowledge, and Vaccination Attitudes and Behaviors for Long COVID Among French Civil Servants: Cross-Sectional Survey
Bibliographic record
Abstract
Background: Long COVID affects millions worldwide, straining health systems and workforce stability. This first nationwide survey among French civil servants combines epidemiological assessment with a Knowledge, Attitudes, and Behaviors approach. Long COVID remains a diagnostic and epidemiological challenge with evolving symptoms and uncertain categorization, particularly among self-suspected cases. Beyond prevalence and risk factors, understanding behavioral dimensions is essential to developing prevention strategies and maintaining workforce resilience. Objective: This study aimed to (1) assess the prevalence of long COVID among French civil servants; (2) identify associated sociodemographic, occupational, and health-related factors; (3) assess disease-related knowledge of long COVID and (4) examine attitudes and behaviors regarding COVID-19 vaccination. Methods: This cross-sectional survey was conducted in 2024 among active or retired civil servants in France. A Knowledge, Attitudes, and Behaviors-validated questionnaire, based on World Health Organization guidelines, was used. Responses were compared across 4 COVID-19 status groups (no COVID, COVID-19 without long COVID, diagnosed long COVID, and suspected long COVID). Statistical analyses included univariate tests and multivariable logistic regressions to identify factors associated with diagnosed or suspected long COVID. Results: Among 3962 eligible respondents, 61 (1.54%; 95% CI 1.20-1.97) reported a formal diagnosis of long COVID and 241 (6.08%; 95% CI 5.38-6.87) without diagnosis. Diagnosed long COVID was significantly associated with long-term sick leave (odds ratio [OR] 1.15, 95% CI 1.03-6.28; P=.04) and long-term illness coverage (OR 0.72, 95% CI 0.27-0.92; P=.03). Suspected long COVID was associated with being in a relationship (OR 1.65, 95% CI 1.08-2.52; P=.02), widowed (OR 2.25, 95% CI 1.18-4.31; P=.01), and uncertain (OR 1.90, 95% CI 1.32-2.74; P<.001) or incomplete COVID-19 vaccination status (OR 1.67, 95% CI 1.16-2.42; P=.01). Knowledge scores differed significantly across groups (ANOVA F3,3476=24.31, P<.001; χ²6=54.92, P<.001), with diagnosed cases showing the highest proportion of high knowledge (13/61, 21%) compared to 12.4% in the non-COVID group. Among 61 diagnosed cases, 36 (59%; 95% CI 46.4-70.5) were vaccinated, 13 (21%; 95% CI 12.9-33.2) intended to get vaccinated, and 12 (20%; 95% CI 11.6-31.3) remained unvaccinated; among suspected cases, these proportions were 173 (71.8%; 95% CI 65.9-77.1), 30 (12.4%; 95% CI 8.8-17.3), and 38 (15.8%; 95% CI 11.6-21.0), respectively. Conclusions: Unlike previous studies that examined the clinical or behavioral factors separately, this nationwide analysis linked epidemiological data with knowledge and vaccination behaviors. Among French civil servants, long COVID remains underdiagnosed, where absenteeism and sick leave threaten essential services. The study highlights disparities in disease-related knowledge, vaccination attitudes, and behaviors, underlining the importance of workplace health education and systematic screening. Vaccination is associated with lower odds of long COVID, reinforcing its preventive value. Thus, findings reveal organizational implications and support workplace-based prevention strategies integrating vaccination promotion, early detection, and health literacy to sustain the resilience of public services.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".