General practitioners’ knowledge, perception and experience on Long Covid: a cross-sectional study in Belgium and Malta
Bibliographic record
Abstract
Abstract Background Over 10% of Covid-19 infected people experience persistent symptoms (Long Covid). As most Long Covid cases are in patients with a mild acute Covid-19 infection, primary health care and general practitioners (GPs) are at the forefront in their care. This study investigated GPs’ knowledge, perception and experience on Long Covid at cross-country level. Methods A cross-sectional study targeting GPs was conducted in Belgium and Malta during mid-2022. An online survey on Long Covid was disseminated. Descriptive and logistic regression analyses were performed. Results The survey was completed by 150 GPs (105 for Belgium, 45 for Malta). In both countries, most GPs reported insufficient scientific knowledge and information on Long Covid diagnosis and treatment. Accessibility to educational material was limited and an awareness-rising campaign was seen as merited, especially by Maltese GPs (OR = 6.81, 95%CI [1.49;31.12]). For diagnosing Long Covid, 54.7% reported the requirement of a positive Covid-19 test, more among Belgian than Maltese GPs (64.3% vs 45.2%, p = 0.036). To assess Long Covid, GPs applied diagnostic criteria by themselves (47.3%) in combination with persistent symptoms. 76.0% GPs reported caring for Long Covid patients, irrespective of practice type and GPs’ country, sex or age (p = 0.353; p = 0.241; p = 0.194; p = 0.058). Although most GPs (94.7%, p = 0.291) stated that Long Covid patients should follow multidisciplinary approach, only 29.8% of them reported to care for these patients by multidisciplinary cooperation and 48.3% by themselves/GP colleagues. Conclusions GPs frequently provide care to Long Covid patients and GPs’ care showed similarities at cross-country level. Although GPs perceived lack of scientific knowledge and educational material on Long Covid, similar diagnostic criteria were noted. Across Europe, uniform evidence-based guidelines, scientific support and training for GPs must be a priority to empower GPs in their Long Covid approach.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".