Activity limitations and healthcare use in people with long COVID-19
Bibliographic record
Abstract
Background: Research describing the impact of long COVID (LC) in low and middle-income countries is lacking. This study described the characteristics of people with LC who experienced activity limitations and their associated healthcare use in Latin America (LA). Methods: Data was collected between Nov 1-Dec 1, 22 with an electronic survey. Individuals who resided in a LA country, had COVID-19, and could read, write, and comprehend Spanish were invited to participate. Sociodemographic characteristics, vaccination status, COVID-19 and LC symptoms, activity limitations and healthcare use were collected. Results: 1178 (48%) of the respondents from 16 countries had LC symptoms (>=3 months), mainly females (68.2%) with mean age of 37.9 (SD 14.9) years. 33% of LC respondents had reduced the time they normally spent on their regular activities (e.g., work, school) and 8% needed help with ADLs (activities of daily living). Respondents with activity limitations were older, had no COVID-19 vaccines, had more comorbidities, had more symptoms, and used more healthcare services (primary care, emergency department and hospitalization) than their counterparts. LC respondents who reduced their usual activities had significantly more difficulty sleeping, chest pain with activity, depression, and problems with concentration, thinking and memory. Those who needed held with ADLs were more likely to have difficulty walking, and SOB at rest. Approximately 60% of respondents who experienced activity limitations saw a specialist and 50% consulted one or more therapists. Conclusions: Our results help clarify the impact of LC on patients’ lives and healthcare systems, which is valuable evidence to inform service planning and resource allocation in alignment with the needs of this population.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".