Factor affecting the activities of daily living among aging people during the COVID-19 pandemic – a structural equation modelling
Bibliographic record
Abstract
Introduction: The activities of daily living (ADLs) are a set of basic skills necessary for self-care. The inability of elderly people to perform ADLs leads to dependence, insecure conditions, and poor quality of life. The COVID-19 pandemic has affected all aspects of the daily life of the elderly. This study aimed to determine the factors associated with ADLs among elderly people during the COVID-19 pandemic using structural equation modelling/path analysis. Material and methods: It was a descriptive-analytical study which had conducted on 487 elderly people who were selected randomly to participate in the study. Data collection tools included a demographic information questionnaire, an activities of daily living questionnaire, a knee pain and personal performance questionnaire Western Ontario and McMaster Universities Osteoarthritis (WOMAC), and the falls efficacy scale, which were completed by interview and self-report methods. SPSS-22 and AMOS software were used for data analysis. Results: < 0.001, root mean square error of approximation = 0.063). These variables explained 64% of the ADL variance. Conclusions: The structures of this model (FOF and WOMAC) can be used as a reference framework to design effective interventions for improving ADLs among elderly people during the COVID-19 epidemic. It is also recommended that a multi-component program be provided, which includes exercise and psychological strategies for this population during the COVID-19 pandemic through online videos, distance health programs, etc.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".