Impediments To Healthcare Workers' Well-being During A Pandemic In Vietnam
Bibliographic record
Abstract
China has just lifted its zero-Covid policy and made it more relaxed, which suggests the end of zero-Covid globally. Vietnam is one of a few countries that implemented zero-Covid at the very beginning, yet we decided to abandon it much sooner. Consequently, a spike in cases followed just after this opening. Healthcare workers (HCW)—the frontliners—are the most harmed and vulnerable, both physically and psychologically. HCW have been playing a vital role in a successful response against multiple Covid-19 pandemics, and patient safety will obviously be in jeopardy when the well-being of HCW is neglected. Nevertheless, literature has reported the anticipated repercussions of Covid-19 on HCW, especially on their mental health and well-being, mostly due to the shortage of resources and equipment, intense work hours, and lack of support for mental health. In this paper, we report on the burden of Vietnamese HCW during the fourth wave of the pandemic, raise awareness among both the community and the authorities, and also deliver feasible solutions adapted from Vietnam. These insights can also be learned by other countries and modified based on local circumstances to help better deal with future outbreaks.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".