Research on the Relationship between Well-being and Personality Traits in the Elderly Based on Canonical Correlation Analysis
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».