Impact of omicron wave and associated control measures in Shanghai on health management and psychosocial well-being of patients with chronic conditions
Bibliographic record
Abstract
The objective of this cross-sectional study was to investigate health management, well-being, and pandemic-related perspectives of chronic disease patients in the context of stringent measures, and associated correlates. A self-report survey was administered during the Omicron wave lockdown in Shanghai, China. Items from the Somatic Symptom Scale (SSS) and Symptom Checklist-90 were administered, as well as pandemic-related items. Overall, 1,775 patients (mostly married females with hypertension) were recruited through a community family physician group. Mean SSS scores were 36.1 ± 10.5/80, with 41.5% scoring in the elevated range (i.e., >36). In an adjusted model, being female, diagnosis of coronary artery disease and arrhythmia, perceived impact of pandemic on life, health condition, change to exercise routine, tolerance of control measures, as well as perception of future and control measures were significantly associated with greater distress. One-quarter perceived the pandemic had a permanent impact on their life, and 44.1% perceived at least a minor impact. One-third discontinued exercise due to the pandemic. While 47.6% stocked up on their medications before the lockdown, their supply was only enough for two weeks; 17.5% of participants discontinued use. Chief among their fears were inability to access healthcare (83.2%), and what they stated they most needed to manage their condition was medication access (65.6%). Since 2020 when we assessed a similar cohort, distress and perceived impact of the pandemic have worsened. Greater access to cardiac rehabilitation in China could address these issues.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".