社会的,感情的,人格的な要因が4つの心理的幸福のプロフィールを形作る:アフィニティ伝播アルゴリズムによる若い成人のクラスタリングアプローチ
Assunta Pelagi1, Chiara Camastra1, Alessia Sarica1
1Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy.
Applied psychology. Health and well-being
|August 30, 2025
まとめ
この研究では,機械学習を用いた若い成人の4つの心理的福祉 (PWB) プロファイルが特定されました. 高いPWBは 認知機能ではなく ポジティブな人格の特徴と 社会的サポートと相関しています
科学分野:
- 心理学
- 機械学習
- 精神 的 な 健康
背景:
- 心理的健康は 精神的健康と生活の質にとって 極めて重要です
- 過去の研究ではPWBと心理的な特徴が調査されましたが,福祉プロファイルは十分に調査されていません.
研究 の 目的:
- 無監督の機械学習を用いて 明確な心理的福祉のプロフィールを特定する.
- PWBプロファイルと認知,人格,感情,行動要因との関連を調べる.
主な方法:
- ヒトコネクトームプロジェクト (HCP) の685人の若い成人に適用された親和伝播 (AP) クラスタリング.
- NIHのツールボックス・エモーション・バッテリーから 複合PWBスコアを利用した.
- 認知機能,人格的特徴,感情的健康,精神的/行動的要因との関連を評価した.
主要な成果:
- 4つの異なるPWBクラスタを特定しました. 低,中低,中高,および高.
- 低いPWBは 高いネガティブな感情と 神経症に関連しています
- 高いPWBは より大きな社会的支持,外向性,好意,そして良心と関連しています
- 認知能力はPWBクラスターで有意に区別されませんでした.
結論:
- 福祉のプロフィールは 主に認知能力ではなく 感情的,社会的,人格的な要因に 影響されます
- 機械学習はデータに基づいた 幸福の理解を可能にします
- 感情的回復力と社会的つながりを高めるための標的型介入の必要性を強調する.
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