関係教育プログラムのコアコンポーネントを特定し,優先順位付け:人工知能 (AI) 支援の体系的なレビューのケーススタディ
Claire Kelley1, Sarah Kelley2, Matthew Rivas-Koehl3
1Child Trends, Bethesda, USA. ckelley@childtrends.org.
まとめ
人工知能 (AI) は,効果的な関係教育プログラムのための研究を効率的にスクリーニングすることによって,予防科学における体系的なレビューを強化します. 人工知能は審査のスピードを上げますが,複雑なデータ分析には人間の専門知識が不可欠です.
科学分野:
- 予防科学 予防科学 予防科学
- 社会的・感情的健康について
- 公共衛生の介入 公共衛生の介入
背景:
- 予防科学は,社会的,感情的,健康的な課題に対する効果的な戦略を特定することを目的としています.
- 予防プログラムのコアコンポーネントを決定することは,ポジティブな結果を出すために極めて重要です.
- システマティック・レビューは研究を統合するために不可欠ですが,時間がかかります.
研究 の 目的:
- 人工知能 (AI) 支援の系統的レビュー方法の使用に関するケーススタディを提示します.
- 健康な結婚と関係教育プログラムの重要な構成要素を特定する.
- 体系的な文献レビューでAIの効率性と正確性を評価する.
主な方法:
- AI支援のシステマティック・レビュー・メソドロジーは,人間関係教育プログラムの重要なコンポーネントを特定するために適用されます.
- AIツールを使用して22,028のリソースのスクリーニング,コーディング,検証.
- 最終データセットは,AI主導のインクルージョンスクリーニング後の268件の関連研究から構成された.
主要な成果:
- AIは,特にインクルージョンスクリーニングにおいて,体系的なレビュープロセスを大幅に加速しました.
- AIによるスクリーニングフェーズでは高い精度が保たれた.
- 人工知能による定性データ抽象化における課題を特定し,人間の監督の必要性を強調した.
結論:
- 人工知能のツールは,予防科学における体系的なレビューの効率と正確性を高めるための有望なアプローチを提供します.
- AIの統合は,効果的な予防戦略の特定を加速することができます.
- 人工知能支援レビューにおける質的データ抽象化には,人間の専門知識が不可欠である.
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