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構造化カリキュラムに基づく発達ネットワーク介入の効果

X Shore1, O Myers1, B Tigges1

  • 1University of New Mexico.

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まとめ
この要約は機械生成です。

構造化された発達ネットワーク介入は、学術医学部教員のネットワーク測定のほとんどを大幅に改善しませんでした。ネットワークの多様性指標の一部は増加を示しましたが、他は減少し、戦略的な計画と長期的なフォローアップの必要性を示唆しています。

キーワード:
発達ネットワーク教員のキャリアアップランダム化比較試験

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科学分野:

  • 学術医学
  • 教員開発
  • ソーシャルネットワーク分析

背景:

  • 発達ネットワークは、学術医学における教員のキャリアアップに不可欠です。
  • 構造化された発達ネットワーク介入に関するランダム化比較試験（RCT）の欠如が存在します。

研究 の 目的:

  • 自己申告された教員のネットワーク測定に対する構造化されたカリキュラムベースの発達ネットワーク介入の効果を評価すること。
  • 介入の影響を評価するためにランダム化比較試験（RCT）デザインを利用すること。

主な方法:

  • 介入は、実践のデジタルコミュニティ（Project ECHO）モデルによって提供されました。
  • 社会的認知理論が、メンター開発の理論的枠組みを提供しました。
  • メンターは、介入、対照（メンター開発プログラムのみ）、および純粋な対照の3つのグループにランダムに割り当てられました。

主要な成果:

  • ほとんどのネットワーク成果は、時間経過とともにグループ間で有意な差は見られませんでした。
  • ネットワーク密度とジェンダーの多様性は、介入群と対照群の両方で24ヶ月で増加しました。
  • 人種/民族の多様性と範囲は、介入群で12ヶ月と24ヶ月で減少しました。

結論:

  • 構造化された発達ネットワーク介入は、ほとんどの教員ネットワーク測定に対して限定的な影響しか示しませんでした。
  • 介入の効果を高めるためには、戦略的なネットワーク計画と長期的なフォローアップが必要になる場合があります。
  • 学術医学部教員のための発達ネットワーク介入を最適化するためには、さらなる研究が必要です。