Resilience and affect balance of empty‐nest older adults with mild cognitive impairment in poor rural areas of Hunan province, China
Bibliographic record
Abstract
AIM: To evaluate the prevalence of mild cognitive impairment (MCI) within the empty-nest older adults population in poor rural areas of the Hunan province of China, and to explore the effects of resilience and affective balance on cognitive functioning within this specific population. METHODS: A cross-sectional, multistage, random cluster survey was administered to participants from March 2013 to December 2014 in the Hunan province. There were a total of 1164 participants. These participants were empty-nest older adults who were residing in poor rural areas of the Hunan province. The data was collected in two stages. In stage 1, the participants were administered the Montreal Cognitive Assessment for screening cognitive impairment. In stage 2, the participants were screened for any potential cognitive impairment, were administered a series of neuropsychological tests and received a definitive diagnosis for MCI, if the criteria were met. Resilience and affect balance were assessed by the Chinese modified version of the Stress Resilience Quotient and the Affect Balance Scale. RESULTS: The prevalence of MCI was 38.40% within this empty-nest older adult population. Significant differences were found between MCI and non-MCI empty-nest older adults specific to resilience and affect balance. Path analysis showed that resilience mediated the relationship between MCI and affect balance. CONCLUSIONS: Resilience and affect balance were less prominent within the MCI empty-nest older adults than those in the non-MCI group. The results suggest that resilience is a mediating variable between MCI and affect balance. Geriatr Gerontol Int 2019; 19: 222-227.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".