Research on the Relationship between Well-being and Personality Traits in the Elderly Based on Canonical Correlation Analysis
Bibliographic record
Abstract
Background With the population ages, the mental health of the elderly has become a hot topic of concern for the whole society. Previous studies have shown that mental health problems in the elderly are closely related to well-being, while personality traits have a greater impact on subjective well-being. However, the internal relationship between the two in the elderly population is still unclear. Objective To explore the relationship between subjective well-being and personality traits of the elderly. Methods From July to August 2022, 511 elderly people in Lincun, Tangxia Town, Dongguan City, Guangdong Province were selected as the subjects by using cluster sampling method, conduct a site survey by using the questionnaire survey. The subjective well-being and personality traits of the elderly were evaluated by the Memorial University of Newfoundland Scale of Happiness (MUNSH) and the China Big Five Personality Scale (CBF-PI-15) respectively. Pearson correlation analysis was used to analyze the correlation between subjective well-being and personality traits of the elderly, canonical correlation analysis was used to construct a standardized canonical correlation model, canonical structure analysis, and canonical redundancy analysis. Results The total score of MUNSH in the elderly was (39.72±7.74) , and the scores of MUNSH in each dimension were positive experience (9.48±3.24) , positive emotion (8.61±2.24) , negative experience (1.44±2.31) , and negative emotion (0.93±1.80) from high to low. The scores of CBF-PI-15 in each dimension of the elderly were agreeableness (14.04±2.60) , extroversion (11.77±4.05) , conscientiousness (10.75±3.57) , openness (7.20±3.90) and neuroticism (6.34±3.22) . Pearson correlation analysis showed that subjective well-being was positively correlated with conscientiousness (r=0.334) and openness (r=0.219) (P<0.05) and negatively correlated with neuroticism (r=-0.223, P<0.05) . Canonical correlation analysis showed that the correlation coefficients of the first and second pairs of canonical correlation variables were 0.476 and 0.331 (P<0.001) . The results of the standardized canonical correlation model construction showed that the correlation between the first canonical correlation coefficient of subjective well-being (U1) of the elderly and the first canonical correlation coefficient of personality traits (V1) mainly manifested negative correlation between positive experience and neuroticism, and positive correlation between positive experience and conscientiousness. The correlation between the standardized canonical correlation coefficient of the second canonical variable of subjective well-being (U2) and the standardized canonical correlation coefficient of the second canonical variable of personality traits (V2) of the elderly mainly manifested positive correlation of positive emotion and negative emotion with neuroticism. The results of canonical structure analysis showed that U1 was strongly correlated with positive emotion, negative emotion, positive experience, and negative experience, while U2 was strongly correlated with negative emotion and negative experience. V1 was strongly correlated with positive experience, conscientiousness, and openness. V2 was strongly correlated with neuroticism and openness. Canonical redundancy analysis showed that U1 explained 5.4% variation in personality traits and V1 explained 12.2% variation in subjective well-being, indicating that personality traits had a greater influence on subjective well-being than subjective well-being. Conclusion On the whole, the elderly from Lincun Tangxia Town, Dongguan City, Guangdong Province hold a positive and optimistic attitude, with a high level of subjective well-being, which is closely related to neuroticism and conscientious personality. In the future, corresponding intervention strategies should be adopted according to different personality characteristics to improve subjective well-being, maintain the mental health of the elderly.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".