847 resultados para qualitative content analysis


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Introduction- This study investigates the prevailing status of Nepalese media portrayal of natural disasters. It is contributing to the development of a disaster management model to improve the effectiveness and efficiency of news production throughout the continuum of prevention, preparedness, response and recovery (PPRR) phases of disaster management. Theoretical framework- Studies of media content often rely on framing as the theoretical underpinning of the study, as it describes how the press crafts the message. However there are additional theoretical perspectives that underline an understanding of the role of the media. This article outlines a conceptual understanding of the role of the media in modern society, the way that this conceptual understanding is used in the crafting of media messages and how those theoretical considerations are applied to the concepts that underpin effective disaster management. (R.M. Entman, 2003; Liu, 2007; Meng & Berger, 2008). Methodology- A qualitative descriptive design is used to analyse the disaster news of Nepal Television (NTV). However, this paper presents the preliminary findings of Nepal Television (a government owned Television station) using qualitative content analysis of 105 natural disaster related news scripts (June 2012-March 2013) based on the framing theory and PPRR cycle. Results- The preliminary results indicate that the media focus while framing natural disasters is dominated by human interest frame followed by responsibility frame. News about response phase was found to be most prominent in terms of PPRR cycle. Limited disaster reporting by NTV has impacted the national disaster management programs and strategies. The findings describe natural disasters are being reported within the limited understanding of the important principles of disaster management and PPRR cycle. Conclusion- This paper describes the current status of the coverage of natural disasters by Nepal Television to identify the frames used in the news content. It contributes to determining the characteristics of effective media reporting of natural disasters in the government owned media outlets, and also leads to including communities and agencies involved in disasters. It suggests the frames which are best suited for news making and how media responds to the different phases of the disaster cycle.

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This thesis addressed issues that have prevented qualitative researchers from using thematic discovery algorithms. The central hypothesis evaluated whether allowing qualitative researchers to interact with thematic discovery algorithms and incorporate domain knowledge improved their ability to address research questions and trust the derived themes. Non-negative Matrix Factorisation and Latent Dirichlet Allocation find latent themes within document collections but these algorithms are rarely used, because qualitative researchers do not trust and cannot interact with the themes that are automatically generated. The research determined the types of interactivity that qualitative researchers require and then evaluated interactive algorithms that matched these requirements. Theoretical contributions included the articulation of design guidelines for interactive thematic discovery algorithms, the development of an Evaluation Model and a Conceptual Framework for Interactive Content Analysis.

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BACKGROUND: High intercoder reliability (ICR) is required in qualitative content analysis for assuring quality when more than one coder is involved in data analysis. The literature is short of standardized procedures for ICR procedures in qualitative content analysis. OBJECTIVE: To illustrate how ICR assessment can be used to improve codings in qualitative content analysis. METHODS: Key steps of the procedure are presented, drawing on data from a qualitative study on patients' perspectives on low back pain. RESULTS: First, a coding scheme was developed using a comprehensive inductive and deductive approach. Second, 10 transcripts were coded independently by two researchers, and ICR was calculated. A resulting kappa value of .67 can be regarded as satisfactory to solid. Moreover, varying agreement rates helped to identify problems in the coding scheme. Low agreement rates, for instance, indicated that respective codes were defined too broadly and would need clarification. In a third step, the results of the analysis were used to improve the coding scheme, leading to consistent and high-quality results. DISCUSSION: The quantitative approach of ICR assessment is a viable instrument for quality assurance in qualitative content analysis. Kappa values and close inspection of agreement rates help to estimate and increase quality of codings. This approach facilitates good practice in coding and enhances credibility of analysis, especially when large samples are interviewed, different coders are involved, and quantitative results are presented.

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The climate change narrative has changed from one of mitigation to one of adaptation. Governments around the world have created climate change frameworks which address how the country can better cope with the expected and unexpected changes due to global climate change. In an effort to do so, federal governments of Canada and the United States, as well as some provinces and states within these countries, have created detailed documents which outline what steps must be taken to adapt to these changes. However, not much is mentioned about how these steps will be translated in to policy, and how that policy will eventually be implemented. To examine the ability of governments to acknowledge and incorporate the plethora of scientific information to policy, consideration must be made for policy capacity. This report focuses on three sectors: water supply and demand; drought and flood planning; and forest and grassland ecosystems, and the word ‘capacity’ as related to nine different forms of policy capacity acknowledged in these frameworks. Qualitative content analysis using NVivo was carried out on fifty four frameworks and the results obtained show that there is a greater consideration for managerial capacity compared to analytical or political capacity. The data also indicated that although there were more Canadian frameworks which referred to policy capacity, the frameworks from the United States actually considered policy capacity to a greater degree.

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In a resource constrained business world, strategic choices must be made on process improvement and service delivery. There are calls for more agile forms of enterprises and much effort is being directed at moving organizations from a complex landscape of disparate application systems to that of an integrated and flexible enterprise accessing complex systems landscapes through service oriented architecture (SOA). This paper describes the analysis of strategies to detect supporting business services. These services can then be delivered in a variety of ways: web-services, new application services or outsourced services. The focus of this paper is on strategy analysis to identify those strategies that are common to lines of business and thus can be supported through shared services. A case study of a state government is used to show the analytical method and the detection of shared strategies.

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Unstructured text data, such as emails, blogs, contracts, academic publications, organizational documents, transcribed interviews, and even tweets, are important sources of data in Information Systems research. Various forms of qualitative analysis of the content of these data exist and have revealed important insights. Yet, to date, these analyses have been hampered by limitations of human coding of large data sets, and by bias due to human interpretation. In this paper, we compare and combine two quantitative analysis techniques to demonstrate the capabilities of computational analysis for content analysis of unstructured text. Specifically, we seek to demonstrate how two quantitative analytic methods, viz., Latent Semantic Analysis and data mining, can aid researchers in revealing core content topic areas in large (or small) data sets, and in visualizing how these concepts evolve, migrate, converge or diverge over time. We exemplify the complementary application of these techniques through an examination of a 25-year sample of abstracts from selected journals in Information Systems, Management, and Accounting disciplines. Through this work, we explore the capabilities of two computational techniques, and show how these techniques can be used to gather insights from a large corpus of unstructured text.

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This is a brief report of a research project, coordinated by me and funded by the Portuguese Government. It studies ‘The Representation of the Feminine in the Portuguese Press’ (POCI/COM 55780/2004), and works on the content analysis of discourse on the feminine in various Portuguese newspapers, covering the time span of February 1st till April 30th 2006. The paper is divided into two parts: in the first part, I will briefly discuss the typology used to code the text units of selected articles; in the second part, I will explore the most expressive percentages of the first two weeks of February for the content analysis of the Diário de Notícias newspaper. These percentages were obtained with the NVivo 6 qualitative data treatment software programme.

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The long-term integrity of protected areas (PAs), and hence the maintenance of related ecosystem services (ES), are dependent on the support of local people. In the present study, local people's perceptions of ecosystem services from PAs and factors that govern local preferences for PAs are assessed. Fourteen study villages were randomly selected from three different protected forest areas and one control site along the southern coast of Côte d'Ivoire. Data was collected through a mixed-method approach, including qualitative semi-structured interviews and a household survey based on hypothetical choice scenarios. Local people's perceptions of ecosystem service provision was decrypted through qualitative content analysis, while the relation between people's preferences and potential factors that affect preferences were analyzed through multinomial models. This study shows that rural villagers do perceive a number of different ecosystem services as benefits from PAs in Côte d'Ivoire. The results based on quantitative data also suggest that local preferences for PAs and related ecosystem services are driven by PAs' management rules, age, and people's dependence on natural resources.

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In this paper we present the development and the implementation of a content analysis model for observing aspects relating to the social mission of the public library on Facebook pages and websites. The model is unique and it was developed from the literature. There were designed the four categories for analysis Generate social capital and social cohesion, Consolidate democracy and citizenship, Social and digital inclusion and Fighting illiteracies. The model enabled the collection and the analysis of data applied to a case study consisting of 99 Portuguese public libraries with Facebook page. With this model of content analysis we observed the facets of social mission and we read the actions with social facets on the Facebook page and in the websites of public libraries. At the end we discuss in parallel the results of observation of the Facebook of libraries and the websites. By reading the description of the actions of the social mission, the general conclusion and the most immediate is that 99 public libraries on Facebook and websites rarely publish social character actions, and the results are little satisfying. The Portuguese public libraries highlight substantially the actions in the category Generate social capital and social cohesion.

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Search engines have forever changed the way people access and discover knowledge, allowing information about almost any subject to be quickly and easily retrieved within seconds. As increasingly more material becomes available electronically the influence of search engines on our lives will continue to grow. This presents the problem of how to find what information is contained in each search engine, what bias a search engine may have, and how to select the best search engine for a particular information need. This research introduces a new method, search engine content analysis, in order to solve the above problem. Search engine content analysis is a new development of traditional information retrieval field called collection selection, which deals with general information repositories. Current research in collection selection relies on full access to the collection or estimations of the size of the collections. Also collection descriptions are often represented as term occurrence statistics. An automatic ontology learning method is developed for the search engine content analysis, which trains an ontology with world knowledge of hundreds of different subjects in a multilevel taxonomy. This ontology is then mined to find important classification rules, and these rules are used to perform an extensive analysis of the content of the largest general purpose Internet search engines in use today. Instead of representing collections as a set of terms, which commonly occurs in collection selection, they are represented as a set of subjects, leading to a more robust representation of information and a decrease of synonymy. The ontology based method was compared with ReDDE (Relevant Document Distribution Estimation method for resource selection) using the standard R-value metric, with encouraging results. ReDDE is the current state of the art collection selection method which relies on collection size estimation. The method was also used to analyse the content of the most popular search engines in use today, including Google and Yahoo. In addition several specialist search engines such as Pubmed and the U.S. Department of Agriculture were analysed. In conclusion, this research shows that the ontology based method mitigates the need for collection size estimation.

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- Background Substance use is common among gay/bisexual men and is associated with significant health risks (e.g. HIV transmission). The consequences of substance use, across the range of substances commonly used, have received little attention. The purpose of this study is to map participant’s beliefs about the effects of substance use to inform prevention, health promotion and clinical interventions. - Methods Participants were interviewed about experiences regarding their substance use and recruited through medical and sexual health clinics. Data were collected though a consumer panel and individual interviews. Responses regarding perceived consequences of substance use were coded using Consensual Qualitative Research (CQR) methodology. - Results Most participants reported lifetime use of alcohol, cannabis, stimulants and amyl nitrite, and recent alcohol and cannabis use. A wide range of themes were identified regarding participant’s thoughts, emotions and behaviours (including sexual behaviours) secondary to substance use, including: cognitive functioning, mood, social interaction, physical effects, sexual activity, sexual risk-taking, perception of sexual experience, arousal, sensation, relaxation, disinhibition, energy/activity level and numbing. Analyses indicated several consequences were consistent across substance types (e.g. cognitive impairment, enhanced mood), whereas others were highly specific to a given substance (e.g. heightened arousal post amyl nitrite use). - Conclusions Prevention and interventions need to consider the variety of effects of substance use in tailoring effective education programs to reduce harms. A diversity of consequences appear to have direct and indirect impacts on decision-making, sexual activity and risk-taking. Findings lend support for the role of specific beliefs (e.g. expectancies) related to substance use on risk-related cognitions, emotions and behaviours.