Status quo的問題，透過圖書和論文來找解法和答案更準確安心。 我們查出實價登入價格、格局平面圖和買賣資訊

Innovating in Healthcare: Creating Breakthrough Services, Products, and Business Models

為了解決Status quo的問題，作者Herzlinger, Regina E. 這樣論述：

Create breakthrough services, products, and business modelsInnovating in Health Care offers effective approaches for designing, reworking, and implementing innovative healthcare services, products, and business models. It will help anyone working in healthcare service or product development, from ho

spitals to startups, to question the status quo in healthcare and implement new solutions that lower costs while increasing both quality and access.Globally, healthcare faces a threefold crisis of unsustainable economics, erratic quality, and unequal access. Just in the U.S., healthcare accounted fo

r 18% of the 2017 GDP and will likely reach nearly 20% by 2025, while hospital-induced deaths have skyrocketed, and tens of millions of people remain uninsured. This book will focus on creating the innovations in healthcare that can meet these needs.- Written by the world''s leading authority on hea

lthcare innovation - Includes success stories in every segment of the health care sector - Presents and applies the Six Factors in the environment that critically affect healthcare innovation- Guides the reader through tailoring a business plan specifically for the new businessDesigned for healthca

re executives, providers, and degree students, Innovating in Health Care is a comprehensive guide for maximizing the viability of a new healthcare product, service, or business. Regina E. Herzlinger is the Nancy R. McPherson Professor of Business Administration at the Harvard Business School. She

was the first woman to be tenured and chaired at Harvard Business School and the first to serve on many corporate health care/medical technology boards. She initiated the courses in health care at HBS and was the first faculty member to be selected by the students as their best instructor. In 2018,

she was awarded the prestigious ACHE Honorary Fellowship which represents the leaders of America’s hospitals and was 1 of the 100 Most Influential People in Healthcare - World Edition by Grupo Midia. In 2014, she launched a Harvard MOOC (Massive Open Online Course), on Innovating in Health Care and

in 2013, a continuing series of conferences, ＂21st-Century Health Care Management Education: Confronting Challenges for Innovation with a Modern Curriculum,＂ sponsored by a group she formed, GENiE (Global Educators Network for Health Care Innovation Education), which has supported the many schools t

hat have introduced courses/programs on Innovating in Health Care. Regi earned her BS degree at MIT and her Doctorate at HBS.

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健康倡議及藥學倡議之教學：概念分析及學習成效評估

為了解決Status quo的問題，作者張鈞婷 這樣論述：

健康倡議被認為是醫療專業人員的社會責任，然而健康倡議的定義在不同醫事職類的研究中有所不同，目前對於如何教導學生健康倡議亦沒有共識。因此，本研究旨在了解文獻中健康倡議的特徵、了解學生對於健康倡議和藥學倡議的認知、了解藥學生學習健康倡議的學習歷程並評估在社區藥局課程前後，學生對於健康倡議的態度和自我效能的變化。本研究由概念分析、質性研究和量性研究三個部分組成。首先，收集教導醫藥衛生學生健康倡議之研究，並使用Rodgers 進化式循環分析法探討健康倡議在文獻中的屬性、因素、結果、替代詞及相關概念，以了解健康倡議在各醫療領域之概念。再者，使用主題分析法分析導入健康倡議之社區藥學課程中藥學生的反思作業

內容，以了解學生對於健康倡議概念的認知、學生學習歷程以及比較學生認知與文獻中健康倡議特徵之差異。最後，在隔年的社區藥學課程以問卷評測學生對於健康倡議之態度及自我效能前後測之差異。研究結果顯示，在概念分析中，健康倡議包含九個屬性，分別為（1）立法：參與立法活動並為政策改變採取行動、（2）溝通：人際溝通或通過媒體進行溝通、（3）合作：與其他醫療專業人員合作、（4）領導：領導一群人、（5）支持：支持個人、社區或公眾、（6）專業：利用知識及專業知識、（7）代表：代表人群的聲音、（8）意識：提高社會正義的意識及（9）獲取：確保資源和資訊的獲取。在質性研究中，學生表示他們學習倡議以發揮專業並改變現狀。此外

，他們認為在課程結束後他們的倡議能力、知識和態度都有所提升。在學生的反思作業中，發現其中部分學生對於辯論議題的反思程度變化大於其他學生，而此變化可能受到學生所分配到的辯論議題以及每位學生個人反思能力所影響。在量性研究中，問卷前後測的結果在三個構念—自我效能（Δ：6.460，p

Feminist Perspectives on Contemporary Zombies, Vampires, and Witches: Radical Monstrosity in Literature, Film, and TV

為了解決Status quo的問題，作者Breuer, Heidi/ Wilson, Natalie 這樣論述：

This book explores three monstrous figures prominent in contemporary popular culture--zombies, vampires, and witches--from a feminist perspective, visiting a range of novels, television series, and films from the 1990s-2010s. Though some popular mainstream texts (from Harry Potter to Twilight) still

participate in historically inherited conventions, feminist analysis reveals that many millennial texts utilize monster-figures to critique regressive patriarchal ideologies and/or champion female monstrosity and (human) female agency. Rather than using zombies, vampires, and witches to conserve th

e status quo, millennial monsters and human heroes, especially female ones, are often deployed to envision a more feminist, egalitarian future with less violence and more social justice. In a culture in which post-feminist voices contend that feminists have achieved their aims and are thus no longer

needed, this book argues instead that there still is a dire need for critical, active feminism. Monsters, creatures both incredibly potent and popular, can serve as conduits through which to lay bare existing problems and reveal possible ways forward. Popular texts like True Blood, Frozen, Malefice

nt, World War Z, and The Walking Dead use powerful female monsters and heroes not only to resist those forces that wish to maintain the status quo, but also to envision alternative socio-cultural formations. This book will contribute to essential discussions in feminism, popular culture, gender and

sexuality studies, cultural studies, film studies, and contemporary literature. Heidi Breuer is Associate Professor of Literature and Writing at California State University, San Marcos, USA. Natalie Wilson is Lecturer in Literature and Writing at California State University, San Marcos, USA.

基於目的地導向之道路潛在危險社交行為預測

為了解決Status quo的問題，作者林明志 這樣論述：

本論文主要開發一套基於目的地導向之道路潛在危險社交行為預測，如行人或車輛無預期性的突然闖入車道、行人不遵守道路規則橫跨馬路等道路危險情境，藉由所發展的深度學習演算策略預測動態物件的短期軌跡，以進一步達到駕駛安全預警輔助系統之功效。首先，為了提取道路環境中動態物件一小段連續時間的辨識結果，故本論文主要是採用深度學習模型進行物件辨識，並於辨識後使用件追蹤演算法，以確保獲得的邊界框為同一行人、四輪車輛或者兩輪車輛。接著我們發展一套基於目的地導向之社交行為預測模型，並搭配自我迴歸訓練策略，以實現物件彼此之間的社交軌跡預測，其中該網路模型主要分成五大部分 (1)特徵提取器；(2)編碼器；(2)目的地導

向預測器；(3)條件變分自動編碼器；(4)解碼器。首先，透過特徵提取器由輸入資訊中提取動態物件與自車彼此間的距離、動態物件速度、動態物件軌跡以及自車的狀態等時序特徵。接著，輸入至編碼器中進行編碼，此編碼器主要由長短期記憶與多頭自注意力機制組成，分別針對目標物件的時序特徵以及社交關係進行編碼。接著，目的地導向預測器則是透過長短期記憶與多頭自注意力機制先行預測未來軌跡，並分別向前回饋給編碼器以輔助特徵編碼生成；同時向後輸出至後續的條件變分自動編碼器，以用來輔助最終的軌跡預測結果。第三部分為條件變分自動編碼器將未來軌跡做為條件，生成符合條件的未來軌跡多模態(multimodal)分佈。最終透過基於多

頭自注意力機制的解碼器，有效預測出更準確的軌跡路徑。最後本文主要是採用TITAN公開資料庫，以進行本文所發展的演算模型驗證與量化分析。經實驗結果發現，本文所提方法其預測軌跡的平均位移誤差(ADE)能有效改善5%、最終位移誤差(FDE)更能有效改善21%，同時最終交並比(FIOU)也提升9%。