テキストベースのカウンセリングにおける感情の変化の異質性を評価する: 潜在的クラス経路分析
Ziru Fu1, Yu Cheng Hsu2, Christian Shaunlyn Chan3
1Department of Social Work and Social Administration, Faculty of Social Sciences, Faculty of Social Sciences, University of Hong Kong, Hong Kong, China (Hong Kong).
Journal of medical Internet research
|September 5, 2025
まとめ
研究者はオンラインカウンセリングにおいて 3つの感情の軌道を特定しました 安定した改善,悪化,そして後には反発です これらのパターンを早期に特定することで テキストベースのメンタルヘルスサポートが強化できます
科学分野:
- デジタルメンタルヘルス
- コンピュータ社会科学
- 臨床心理学
背景:
- オンラインのテキストベースのカウンセリングは増えていますが,その性質はユーザーの感情的な変化を監視する上で課題を提示しています.
- 匿名性とテキスト形式は,パーソナライズされた介入を阻害し,サービスの効果とユーザーの満足度に影響を与える可能性があります.
研究 の 目的:
- オンラインのテキストベースのカウンセリングで助けを求める人々の 異なる感情の軌道を特定する.
- 異なる感情の軌道のメンバーシップに関連した重要な変数を調査する.
主な方法:
- 6207回のカウンセリングセッションで成長混合モデル (GMM) を用いて潜伏クラス経路分析.
- チャットGPTによってラベル付けされた助けを求めるメッセージのセンチメントスコアは,軌道をモデル化するために使用されました.
- マルチノミアルロジスティック回帰は,クラスメンバーシップに関連する変数を特定しました.
主要な成果:
- 安定した改善 (18.9%),悪化 (18.0%),後には反発する (63.1%) の3つの異なる景気が見られました.
- 悪化経路は,自殺念頭,家族/身体的健康上の懸念,およびセッションの早期離脱と有意に関連していました.
結論:
- オンラインのテキストベースのカウンセリングでは 異なるセッション内の感情の軌跡が存在します
- これらの経路を早期に特定することで カウンセラーが介入を調整し デジタルメンタルヘルスサービスの効果と満足度を向上させることができます
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