The Impact of the COVID-19 Pandemic on the Mental Health of Health Workers Treating Patients with Kidney Diseases in Latin America (LA): Analysis from GlomCon Latin America Working Group (LGlomCon)
Bibliographic record
Abstract
Background: The rapid spread of the COVID-19 pandemic into LA countries where health systems were already facing major limitations might further challenge their physician’s emotional and mental wellbeing. We aimed to describe the perception of health workers managing kidney diseases in the context of the COVID-19 pandemic. Methods: Descriptive analysis extracted from an online survey carried out among nephrologists, renal pathologists, and other health workers treating kidney diseases between May 20-27, 2020 from sixteen Spanish speaking Latin American countries divided into 6 categories. We present the results for the mental health category. Results: 430 responses were obtained of which 360 (84%) were considered for analysis. The participants were mainly nephrologists 276 (86%), renal pathologists 13 (4%), and physicians in training 11 (3%). Ages ranged between 30-49 years old in 271 (75%), mostly working on tertiary centers 258 (71%). 329 (90%) participated in inpatient care. 277 (86%) considered that the COVID-19 pandemic has impacted their mental health. Prevailing symptoms were anxiety, insomnia, and depression, with 75.2%, 42.5%, and 18.2%, respectively. Physical or verbal violence from the community was reported by 18 (5%) of the participants because they were seen as a source of viral transmission. 179 (55%) considered personal protective equipment (PPE) was sufficiently provided and 275 (79%) had to invest up to 20% of their income to obtain PPE. In addition, 144 (44%) of the respondents reported a shortage of COVID-19 tests and only 99 (30%) felt their hospital was well equipped to care for COVID-19 patients. 126 (39%) of the health workers responded that they received adequate training, while 105 (32%) endorsed they did not feel prepared in the management of patients with COVID-19. Conclusions: This survey reveals the considerable impact that the COVID-19 pandemic is generating among physicians treating patients with kidney diseases in LA. Possible aggravating factors also found in our survey included lack of testing, PPE availability, and overall hospital preparedness. Funding: Private Foundation Support
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".