目的 ミオチュブラーミオパチーのAくん（5歳男児）は長期入所開始後、職員と関わる中で感情・要求の表現の幅が広がった。入所後5カ月間のAくんの感情・要求の表出の変化と、表出能力に影響を与えた介入を明らかにする。 方法 Aくんを担当する看護師、生活支援員、作業療法士、理学療法士、言語聴覚士、臨床心理士が自由記載した入所後5カ月間の記録を収集し、「1．入所時の感情・要求の表出を評価した内容」、「2．1の評価に対するAくんへの介入内容」、「3．入所後5カ月間の介入を行った評価内容」に分類した。さらにこれを1）吸引、2）排泄、3）発語、4）他者の関心の引き方、5）遊びの5項目に分類し考察した。この研究はC病院の倫理審査委員会の承認を得たうえで、Aくんの保護者の同意を得て実施した。 成績 担当職員らから収集したデータをそれぞれ感情・要求の表出について分類し、それを再度5項目に分類して表にまとめた。分類した5項目について考察をした結果、Aくんの感情・要求の表出能力は向上したといえる。感情・要求の表出能力に影響を与えたのは「職員が統一してAくんが動作や発語の模放、自発的なやり取りを繰り返すように関わること」、「職員が保育的・教育的な視点をもちながらAくんと関わり、さらにあらゆる年代の他者との関わりや玩具での遊びを通してAくんの社会性の発達を促すこと」、「専門的に評価されたAくんの能力と発達段階に合わせた関わりをするための、そしてリハビリテーションを通して身体的機能や発語の能力の向上を促すためのリハビリスタッフとの協働」といった3つの介入が影響していると推測される。 結論 職員の行った介入がそれぞれ影響し合ったことで、Aくんの感情・要求の表出能力は向上したと考えられる。

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Content regarding various illegal activities, such as weapon and drug trafficking, is shared on the dark web. Most of the illegal content is distributed on anonymous networks that cannot be directly accessed from the World Wide Web. A number of studies have been conducted on the network structure of the World Wide Web since its advent. Similar to the World Wide Web, the dark web is connected by hypertext transfer protocol (http); this makes it possible to use the methods developed for the web in the dark web. Many studies have investigated the dark web and its network structure. However, few studies have focused on the visualization of the dark web network structure, and there have been no studies investigating the temporal changes in the network structure. In this study, to understand the hypertext markup language (html) network structure of the dark web, we created and visualized a graph of the html hyperlink relations of the Tor network, which is popular on the dark web. We then compared the insights gained from graph centrality metrics with those gained from visualizations. The analyzed dataset comprised 25,270,157 pages of html text files crawled from the Tor network by breadth-first search from June 1, 2018, to January 30, 2021. Subsequently, we acquired half-yearly snapshots from the collected data and investigated the change in the dark web network over time using a time-series graph. Then, we derived the centrality metrics from the created graph data and confirmed the differences between the centrality metrics and visualizations. The results obtained in this study provided new insights into the dark web. First, we found that the dark web fluctuated significantly; the structure of the dark web network was more strongly interconnected. Second, most of the nodes that had increased in the past two years may have disappeared rapidly after May 2020. Third, analysis of each snapshot revealed that the proportion of highly volatile domains increased from 40% to 75% during the observation period. Fourth, after calculating the network centrality metrics from each snapshot and comparing the transition of hub nodes in chronological order, we observed that the importance of link-collection sites as the main information retrieval method used in the dark web decreased. Finally, we estimated the size of the dark web based on our observed dark web measurements using the mark-recapture method. To the best of our knowledge, this is the first study to use the mark-recapture method to estimate the size of the dark web network.

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