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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃恆獎 | zh_TW |
| dc.contributor.advisor | Heng-Chiang Huang | en |
| dc.contributor.author | 袁治平 | zh_TW |
| dc.contributor.author | Chih-Ping Yuan | en |
| dc.date.accessioned | 2024-09-11T16:25:00Z | - |
| dc.date.available | 2024-09-12 | - |
| dc.date.copyright | 2024-09-11 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-08-08 | - |
| dc.identifier.citation | 中文文獻
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/95542 | - |
| dc.description.abstract | 台灣在2025年即將邁入超高齡化社會，老年化及慢性病的人口逐年上升，但台灣目前醫護人力正面臨緊缺的狀態，慢性病病患的照護的需求只會日益增加。然而生成式AI(Generative AI, GenAI)在ChatGPT問世之後，該技術的應用受到廣大的討論。此現象讓研究者感到好奇，GenAI技術性的突破，是否符合慢性病病患的臨床需求。因此，本研究從慢性病病患必須面對長期心理的壓力與挫折的角度出發，針對心理學的因應策略審視過往的文獻，提出一個結合因應策略中兩個主要的策略：「問題聚焦因應策略」及「情緒聚焦因應策略」與相關變數之模型，藉此探討慢性病病患是否會因為GenAI提供因應策略中的兩種面向的相關因子，進而提升對於AI健康聊天機器人的偏好與使用意願。
本研究於 2024年 6 月 10 日至 6 月 24 日期間，透過便利抽樣法於社群平台發放 SurveyCake 平台問卷連結以蒐集相關樣本，最終有效樣本數為 187 筆(回應率 64.71%)。研究中使用描述統計分析、結構方程式模型分析進行資料分析，並發現：若AI健康聊天機器人可提供「問題聚焦因應策略」及「情緒聚焦因應策略」，慢性病病患對其偏好程度將有正向的影響，而這樣的偏好程度會進而影響其使用意願。此外，情緒聚焦因應策略的影響程度略高於問題聚焦因應策略。另外，研究中也發現：年齡、教育與焦慮程度並不會影響患者對於AI健康聊天機器人的偏好與使用意願。 整體而言，本研究透過病患因應壓力與焦慮的策略，來進一步探討GenAI是否符合慢性病病患的需求，而研究中也證實無論是問題聚焦因應策略或情緒聚焦因應策略，確實都能提升病患的偏好程度，且不受其對於疾病之焦慮程度而有所影響。本研究試圖提供GenAI於醫學衛教上可行之應用方向，同時也強調GenAI可透過有效的應對策略來滿足慢性病病患需求的潛力。 | zh_TW |
| dc.description.abstract | In 2025, Taiwan is approaching a super-aged society with a growing elderly population and an increasing prevalence of chronic diseases. However, Taiwan’s healthcare system is facing severe shortages in personnel, and the demand for care for chronic disease patients is only set to rise. Following the emergence of Generative AI (GenAI), particularly ChatGPT, extensive discussion has arisen regarding its applications. This phenomenon has sparked curiosity among researchers about whether the technological breakthroughs of GenAI meet the clinical needs of chronic disease patients.
Therefore, this study begins from the perspective of the long-term psychological stress and challenges faced by chronic disease patients. It reviews the literature on psychological coping strategies and proposes a model that integrates two primary coping strategies: “problem-focused coping” and “emotion-focused coping,” along with related variables. The study explores whether chronic disease patients’ preferences for AI health chatbots and their willingness to use them can be enhanced by GenAI’s provision of these coping strategies. From June 10 to June 24, 2024, the study employed convenience sampling via SurveyCake platform links distributed on social media to collect relevant samples. The final dataset included 187 valid responses (with a response rate of 64.71%). Data analysis utilized descriptive statistics and structural equation modeling. The findings indicate that the provision of “problem-focused coping” and “emotion-focused coping” strategies by AI health chatbots positively influences chronic disease patients’ preferences for these technologies, affecting their willingness to use them. Additionally, the impact of emotion-focused coping strategies is slightly more substantial than that of problem-focused coping strategies. Furthermore, the study found that age, education, and anxiety level do not significantly influence patients’ preferences for or willingness to use AI health chatbots. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-09-11T16:25:00Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-09-11T16:25:00Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 II
中文摘要 III ABSTRACT IV 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與問題 3 第三節 研究流程 3 第二章 文獻探討 6 第一節 生成式人工智慧在健康管理的研究 6 第二節 病患焦慮與生成式人工智慧 7 第三節 因應策略 8 第四節 問題聚焦因應策略 9 第五節 情緒聚焦因應策略 12 第六節 偏好程度和使用意願 15 第三章 研究方法 18 第一節 影片設計 18 第二節 問卷設計及抽樣 22 第三節 變數定義與衡量與研究架構 25 第四節 資料分析方法 33 第四章 研究結果 35 第一節 描述統計分析 35 第二節 結構方程模式分析結果 40 第五章 結論與建議 49 第一節 主要研究發現 49 第二節 學術貢獻與實務意涵 50 第三節 研究限制與未來研究方向 51 參考文獻 53 中文文獻 53 英文文獻 54 附錄：問卷內容 64 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 生成式AI與醫療衛教：探討慢性病病患之科技介入與焦慮處理策略 | zh_TW |
| dc.title | Generative AI and Health Education: Exploring Technological Interventions and Anxiety Coping Strategies for Chronic Disease Patients | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 潘令妍 | zh_TW |
| dc.contributor.coadvisor | Ling-Yen Pan | en |
| dc.contributor.oralexamcommittee | 王仕茹;胡凱焜 | zh_TW |
| dc.contributor.oralexamcommittee | Shih-Ju Wang;Kai-Kun Hu | en |
| dc.subject.keyword | 生成式AI,醫療衛教,慢性病病患,問題聚焦因應策略,情緒聚焦因應策略,聊天機器人,偏好,使用意願, | zh_TW |
| dc.subject.keyword | Generative AI,healthcare education,chronic disease patients,problem-focused coping strategies,emotion-focused coping strategies,chatbot,preference,use intention, | en |
| dc.relation.page | 71 | - |
| dc.identifier.doi | 10.6342/NTU202403976 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-08-12 | - |
| dc.contributor.author-college | 進修推廣學院 | - |
| dc.contributor.author-dept | 生物科技管理碩士在職學位學程 | - |
| 顯示於系所單位： | 生物科技管理碩士在職學位學程 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-2.pdf 目前未授權公開取用 | 3.02 MB | Adobe PDF | 檢視/開啟 |
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