The correlation between self-care and quality of life of olderpeople in Pol-e Dokhtar City during the COVID-19 pandemic
Bibliographic record
Abstract
Literature search, G -Funds CollectionBackground.The outbreak of COVID-19 reduced social interactions and access to healthcare centers, affecting older people's self-care ability and quality of life.Low quality of life was associated with a higher mortality rate in older people with COVID-19; thus, large measures should be taken in this field.Objectives.The present study aimed to determine the correlation between self-care at home and older people's quality of life in Pol-e Dokhtar City during the COVID-19 pandemic.Material and methods.The present cross-sectional and descriptive study was conducted by random sampling on 300 older people in Pol-e Dokhtar City.Three questionnaires acquired the data: the demographic information questionnaire, the standard older people self-care questionnaire and the World Health Organization Quality of Life-BREF (WHOQoL-BREF) questionnaire, which were analyzed using SPSS 22 software.Results.The total self-care score was low, and older age was associated with decreased self-care and quality of life.An increased education level enhanced the levels of self-care and quality of life.The psychological and social self-care scores were directly associated with the rise in the quality of life score.The use of insurance increased self-care and quality of life.Retired older people had higher self-care, but employed older people had a higher quality of life.Conclusions.Variables such as psychological and social self-care could predict older people's quality of life during the COVID-19 pandemic.Therefore, educational interventions, social-cultural and recreational sports activities and spiritual, material and emotional support should be done to improve older people's self-care and quality of life.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".