医学生の心理的回復力の秘密:機械学習と経路分析の二重視野
Wenyu Su1, Huiyu Jia1, Wenjing Chang1
1School of Public Health, Shandong Second Medical University, Weifang 261053 Shandong, China.
International journal of medical informatics
|September 5, 2025
まとめ
医学生
科学分野:
- 医学教育
- 心理学
- 医療における機械学習
背景:
- 医学部生は大きなストレスに直面し 将来の医療従事者の精神的健康に 影響を及ぼします
- 心理的回復力を高めることは 職業上の課題に対処するために不可欠です
- リスクのある生徒を特定し 回復力メカニズムを理解することが重要です
研究 の 目的:
- 心理的回復力が低い医学生を特定する
- 医学生の心理的回復力に影響を与えるメカニズムを探る
- 心理的回復力を予測する 機械学習モデルを比較する
主な方法:
- 生徒のデータを用いてXGBoost,LR,DT,RFモデルを開発し比較した.
- SHAPLEY ADDITIVE EXPLANATIONS (SHAP) をモデル解釈のために使用した.
- 介入の経路を特定するために調停分析を行った.
主要な成果:
- XGBoostモデルは優れた予測性能を示した (AUC=0. 856).
- スマートフォン依存症と睡眠障害は 回復力に悪影響を及ぼします
- 社会的支持は回復力と正に相関し 他の要因との関係を媒介する.
結論:
- 認知された社会的支援や スマートフォン依存症 睡眠障害は 医学生の回復力の 重要な予測要因です
- 社会的支援は 行動要因を通して 直接的・間接的に 回復力に影響します
- 生徒の精神的幸福を高めるための 標的型の介入を促します
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