Social Disengagement in Post‐Pandemic China: A Translation, Validation and Cross‐Cultural Comparison of the Pandemic Disengagement Syndrome Scale
Bibliographic record
Abstract
ABSTRACT Background In contrast to abundant research on the various acute mental effects of COVID‐19, the long‐term influences of the pandemic are still underexplored in China owing to the paucity of assessment tools. The Pandemic Disengagement Syndrome Scale (PDSS) assesses people's social disengagement as a lasting psychological consequence in Western countries during the post‐COVID‐19 pandemic era. However, its generalizability across cultures is untested. Objectives The present studies aimed to validate Chinese PDSS and compare disengagement syndrome levels among China, the United States and Italy. Method In Study 1, a Chinese version of the PDSS was developed, psychometric properties including factor structure, internal consistency, measurement invariance across gender and country, discriminant validity, and test‐retest reliability were tested. Study 2 examined demographic differences in the pandemic disengagement syndrome in China and compared Chinese PDSS scores and those in the United States and Italy (Ns = 415US, 455Italy, 826China). Results and Conclusion The findings indicated that disengagement syndrome may exist among Chinese people even substantially after the acute phases of the pandemic. Meanwhile, the Chinese PDSS demonstrating acceptable psychometric features can be a valid instrument to assess the syndrome. Several possible reasons for the persistence of disengagement in China are discussed.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".