Impacts of the COVID-19 pandemic on patients with chronic conditions in Vietnam: A cross-sectional study
Bibliographic record
Abstract
Objectives We assess the impact of the COVID-19 pandemic on health, treatment adherence and expectations of patients with chronic diseases in Vietnam. Methods We conducted a national cross-sectional study using a questionnaire survey, distributed through social networks and presented on Google Forms. The survey was performed during two months of the most stringent social distancing in Vietnam (between 21 July and 21 September 2021). Results Most of the participants said that the COVID-19 epidemic had affected their daily activities (91.9%), health (53.6%), sleep behavior (52.3%), and mental health (79.8%). During social distancing in Vietnam, three-quarter could not go to hospitals for periodic health examination; nearly half of respondents did not do daily physical activity; a quarter of respondents did not adhere to recommended diet plan. Factors associated with the effect of the COVID-19 epidemic on patient's health included those living in Ho Chi Minh City ( p = 0.015), lived alone ( p = 0.027), uncontrolled chronic conditions ( p < 0.001), treatment dissatisfaction or experienced anxiety/stress ( p < 0.001). Factors associated with medication adherence included the elderly ( p = 0.015), having periodic health examination ( p = 0.012), direct consultation ( p = 0.003), and telemedicine ( p = 0.007). Conclusion This study highlights the urgent need for better chronic management strategies for the new post-COVID era in the future.
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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 | 0.001 |
| 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 | 0.000 |
| 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".