MONITORING POST-COVID-19 SEQUELAE IN PRIMARY HEALTH CARE: A SCOPING REVIEW
Bibliographic record
Abstract
Post-COVID-19 sequelae include signs, symptoms and/or conditions that persist or emerge four weeks after infection with the SARS-CoV-2 virus and Primary Health Care (PHC) has a fundamental role in conducting a careful approach to physical and psychosocial sequelae that persist in individuals who survived the disease, contributing to the control and mitigation of damage resulting from the pandemic. This research aims to identify and map the care process of monitoring and multidisciplinary follow-up of post-COVID-19 sequelae within the global PHC. This is a scoping review, following the methodological recommendations of the Joanna Briggs Institute (JBI), focused on understanding how care occurs for people with post-COVID-19 sequelae in PHC. The database search was conducted during July and August 2023, including research articles, theses, dissertations, and official documents. Eight studies were selected for analysis. Publications occurred in 2021 (25%), 2022 (37.5%), and 2023 (37.5%), with a predominance of studies from the United States (25%), although other countries such as Canada, Chile, Germany, Brazil, Spain, and the United Kingdom were also represented. Regarding the level of evidence, most studies (50%) were classified as level 3 (single qualitative study). Among the most severe complications reported in the studies are Acute Respiratory Distress Syndrome, hypoxia, cardiac arrhythmias, myocarditis, pulmonary embolism, and neurological, hepatic, and renal issues. Additionally, more common symptoms such as fatigue, dyspnea, chest discomfort, and psychological issues like anxiety and depression highlight the virus's prolonged systemic effects. It was identified that in PHC, managing post-COVID-19 sequelae requires an expansion of health teams and enhanced psychological support. Continuous identification and classification of symptoms are essential for effective referral, aiming for integrated and persistent care. Challenges such as delays in care, communication problems, and underreporting of symptoms point to the need for a more holistic and multidisciplinary approach. This should include enhanced professional training and strengthening of PHC services to mitigate the late effects of COVID-19. Lastly, this review underscores the complexity of post-COVID-19 sequelae and the vital role of PHC in its management, necessitating a patient-centered multidisciplinary approach. The efficacy of PHC depends on the availability of resources, a well-trained team, and effective coordination with other levels of care. The need for contextual adaptations in PHC services and collaboration among healthcare systems to improve accessibility and outcomes of post-COVID rehabilitation is emphasized, highlighting the challenges and increasing demands faced by healthcare professionals.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".