The Diary

Pepys, Samuel

doi:10.1093/oseo/instance.00174762

Cited by 39 publications

(30 citation statements)

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“…Historians draw on FUPS data all the time. As an historical record, Pepys’ diary [ 43 ] is flawed in that it is not representative of all people living in this period, uncertain in that the author is a highly unreliable narrator, proximate in that, even where Pepys is most honest, it is still refracted through his perception, and sparse on some of the key details we might like to know about. This does not stop it being a source much pored over for insights into seventeenth-century life.…”

Section: Analysing Fups Data: the Example Of Cyp Iaptmentioning

confidence: 99%

Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: learning from the case of child mental health

Wolpert

Rutter

2018

BMC Med

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The use of routinely collected data that are flawed and limited to inform service development in healthcare systems needs to be considered, both theoretically and practically, given the reality in many areas of healthcare that only poor-quality data are available for use in complex adaptive systems. Data may be compromised in a range of ways. They may be flawed, due to missing or erroneously recorded entries; uncertain, due to differences in how data items are rated or conceptualised; proximate, in that data items are a proxy for key issues of concern; and sparse, in that a low volume of cases within key subgroups may limit the possibility of statistical inference. The term ‘FUPS’ is proposed to describe these flawed, uncertain, proximate and sparse datasets. Many of the systems that seek to use FUPS data may be characterised as dynamic and complex, involving a wide range of agents whose actions impact on each other in reverberating ways, leading to feedback and adaptation. The literature on the use of routinely collected data in healthcare is often implicitly premised on the availability of high-quality data to be used in complicated but not necessarily complex systems. This paper presents an example of the use of a FUPS dataset in the complex system of child mental healthcare. The dataset comprised routinely collected data from services that were part of a national service transformation initiative in child mental health from 2011 to 2015. The paper explores the use of this FUPS dataset to support meaningful dialogue between key stakeholders, including service providers, funders and users, in relation to outcomes of services. There is a particular focus on the potential for service improvement and learning. The issues raised and principles for practice suggested have relevance for other health communities that similarly face the dilemma of how to address the gap between the ideal of comprehensive clear data used in complicated, but not complex, contexts, and the reality of FUPS data in the context of complexity.

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Section: Analysing Fups Data: the Example Of Cyp Iaptmentioning

confidence: 99%

Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: learning from the case of child mental health

Wolpert

Rutter

2018

BMC Med

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“…Specifically, published diary writers of the time were elites and usually well known and either part of/well connected to the church and/or monarchy. For example, the Minister of Parliament Samuel Pepys (1898 [1660]) kept a diary which has become well known. Pepys makes clear his commitment to both church and monarchy in his diary.…”

Section: Resultsmentioning

confidence: 99%

Comparative Process-oriented Research Using Social Media and Historical Text

Murthy

2017

Sociological Research Online

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This article evaluates whether we can use process-oriented theory to conduct comparative, historical social media research. There is a lack of theoretically informed approaches to studying recently digitized historical text with contemporary social media. This article argues that such perspectives are needed and extends Norbert Elias’ notions of ‘sociogenesis’ and ‘psychogenesis’ into data-driven research. Canonical process-oriented researchers such as Elias used mixed-methods approaches, including visual maps and quantitative surveys. By comparing 17th-century digitized diaries and 5 million digitized books from Google Books with contemporary tweet data, this study provides a successful case of comparing tweets with historical printed text at a big data scale. Moreover, quantitative methods are important to process-oriented methodologies and can be extended to big data empirical sources. An important finding is that there are similarities in the curation of everyday life in elite historical diaries and in more democratic forms of contemporary social media. Although accessibility and volume of content have changed over time from historical text to tweets, we found that there is a marked preference for certain words associated with communal sentiment over the centuries.

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“…They intend to admit as many as will venture their money, and so make themselves a company, £250 is the lowest share for every man.'' 27 The corporation was even older at the common law, being a form of self-government for small communities such as cities, towns, charitable projects in health (hospitals) and education (schools and colleges). A corporation was an extension of sovereign authority, established by charter from the crown, usually to empower collective activity with fiscal autonomy.…”

Section: Creating a Special Office For The Protection Of Investorsmentioning

confidence: 99%

Fiduciary Duties as a Helpful Guide to Ethical Decision-Making in Business

Young¹

2007

J Bus Ethics

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The Diary

Cited by 39 publications

References 0 publications

Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: learning from the case of child mental health

Using flawed, uncertain, proximate and sparse (FUPS) data in the context of complexity: learning from the case of child mental health

Comparative Process-oriented Research Using Social Media and Historical Text

Fiduciary Duties as a Helpful Guide to Ethical Decision-Making in Business

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