Predictors of basic and instrumental activities of daily living among older adults with multiple chronic conditions
Bibliographic record
Abstract
BACKGROUND: Understanding the predictors of functional status can be useful for improving modifiable predictors or identifying at-risk populations. Researchers have examined the predictors of functional status in older adults, but there has not been sufficient study in this field in older adults with multiple chronic conditions, especially in Iran. Consequently, the results of this body of research may not be generalizable to Iran. Therefore, this study was conducted to determine the predictors of functional status in Iranian older adults with multiple chronic conditions. METHODS: In this cross-sectional study, 118 Iranian older adults with multiple chronic conditions were recruited from December 2022 to September 2023. They were invited to respond to questionnaires inquiring about their demographic and health information, basic activities of daily living (BADL) and instrumental activities of daily living (IADL), depression and cognitive status. The predictors included age, gender, marital status, education, number of chronic conditions, and depression. Descriptive and analytical statistical tests (univariate and multiple regression analysis) were used to analyze the data. RESULTS: The majority of participants were married (63.9%) and women (59.3%). Based on the results of the multiple regression analysis, age (B=-0.04, P = 0.04), depression (B=-0.12, P = 0.04), and IADL (B = 0.46, P < 0.001) were significant predictors for functional status in terms of BADL. Also, marital status (B=-0.51, P = 0.05), numbers of chronic conditions (B=-0.61, P = 0.002), and BADL (B = 0.46, P < 0.001) were significant predictors for functional status in terms of IADL. CONCLUSION: The findings support the predictive ability of age, marital status, number of chronic diseases, and depression for the functional status. Older adults with multiple chronic conditions who are older, single, depressed and with more chronic conditions number are more likely to have limitations in functional status. Therefore, nurses and other health care providers can benefit from the results of this study and identify and pay more attention to the high risk older adult population.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".