Impact of COVID-19 Pandemic on Rheumatology Practice in Latin America
Bibliographic record
Abstract
OBJECTIVE: To describe the effect of the coronavirus disease 2019 (COVID-19) pandemic on Latin American rheumatologists from a professional, economic, and occupational point of view. METHODS: We conducted an observational cross-sectional study using an online survey sent to rheumatologists of each non-English-speaking country member of the Pan American League of Rheumatology Associations (PANLAR). A specific questionnaire was developed. RESULTS: Our survey included 1097 rheumatologists from 19 Latin American countries. Median (IQR) age of respondents was 48 (40-59) years and 618 (56.3%) were female. Duration of practice since graduation as a rheumatologist was 17 years, and 585 (53.3%) were aged < 50 years. Most rheumatologists worked in private practice (81.8%) and almost half worked in institutional outpatient centers (55%) and inpatient care (49.9%). The median number of weekly hours (IQR) of face-to-face practice before the pandemic was 27 (15-40) hours, but was reduced to 10 (5-20) hours during the pandemic. Telehealth was used by 866 (78.9%) respondents during the pandemic. Most common methods of communication were video calls (555; 50.6%), telephone calls (499; 45.5%), and WhatsApp voice calls (423; 38.6%). A reduction in monthly wages was reported by 946 (86.2%) respondents. Consultation fees also were reduced and 88 (8%) rheumatologists stated they had lost their jobs. A reduction in patient adherence to medication was reported by nearly 50% of respondents. Eighty-one (7.4%) rheumatologists received a COVID-19 diagnosis and 7 (8.6%) of them were hospitalized. CONCLUSION: The COVID-19 pandemic has reshaped rheumatology practice in Latin America and has had a profound effect on rheumatologists' behaviors and clinical practice.
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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 | 0.003 |
| 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".