Behavioral and Mental Responses towards the COVID-19 Pandemic among Chinese Older Adults: A Cross-Sectional Study
Bibliographic record
Abstract
The novel COVID-19 pandemic spread quickly and continuously influenced global societies. As a vulnerable population that accounted for the highest percentage of deaths from the pandemic, older adults have experienced huge life-altering challenges and increased risks of mental problems during the pandemic. Empirical evidence is needed to develop effective strategies to promote preventive measures and mitigate the adverse psychological impacts of the COVID-19 pandemic. This study aimed to investigate the behavioral responses (i.e., preventive behaviors, physical activity, fruit and vegetable consumption) and mental responses (i.e., depression and loneliness) towards the COVID-19 pandemic among Chinese older adults. A further aim was to identify the associations among demographics, behavioral responses, and mental responses. Using a convenience sampling approach, 516 older adults were randomly recruited from five cities of Hubei province in China. Results of the cross-sectional survey showed that 11.7% of participants did not adhere to the WHO recommended preventive measures, while 37.6% and 8.3% of participants decreased physical activity and fruit–vegetable consumption respectively. For mental responses, 30.8% and 69.2% of participants indicated significantly depressive symptoms and severe loneliness, respectively. Participants’ behavioral and mental responses differed significantly in several demographics, such as age group, living situation, marital status, education levels, household income, medical conditions, and perceived health status. Demographic correlates and behavioral responses could significantly predicate the mental response with small-to-moderate effect sizes. This is the first study to investigate the characteristics of behavioral and mental responses of Chinese older adults during the COVID-19 pandemic. Research findings may give new insights into future developments of effective interventions and policies to promote health among older adults in the fight against the pandemic.
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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 | 0.002 |
| 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 | 0.001 | 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".