Dataset Information

Waste management CDM projects barriers NVivo 10® qualitative dataset.

ABSTRACT: This article contains one NVivo 10® file with the complete 432 projects design documents (PDD) of seven waste management sector industries registered as Clean Development Mechanism (CDM) under United Nations Framework Convention on Climate Change (UNFCCC) Kyoto Protocol Initiative from 2004 to 2014. All data analyses and sample statistics made during the research remain in the file. We coded PDDs in 890 fragments of text, classified in five categories of barriers (nodes): technological, financial, human resources, regulatory, socio-political. The data supports the findings of author thesis [1] and other two indexed publication in Waste Management Journal: "The financial attractiveness assessment of large waste management projects registered as clean development mechanism" and "The declared barriers of the large developing countries waste management projects: The STAR model" [2], [3]. The data allows any computer assisted qualitative content analysis (CAQCA) on the sector and it is available at Mendeley [4].

SUBMITTER: Bufoni AL

PROVIDER: S-EPMC5655383 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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Waste management CDM projects barriers NVivo 10® qualitative dataset.

Bufoni André Luiz AL de Sousa Ferreira Aracéli Cristina AC Oliveira Luciano Basto LB

Data in brief 20171010

This article contains one NVivo 10® file with the complete 432 projects design documents (PDD) of seven waste management sector industries registered as Clean Development Mechanism (CDM) under United Nations Framework Convention on Climate Change (UNFCCC) Kyoto Protocol Initiative from 2004 to 2014. All data analyses and sample statistics made during the research remain in the file. We coded PDDs in 890 fragments of text, classified in five categories of barriers (nodes): technological, financia ...[more]

PMID: 29085874

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Project description:To explore changes in healthcare professionals' views about the diagnosis and management of heart failure since a study in 2003.Focus groups and a national online cross-sectional survey.Focus groups (n=8 with a total of 56 participants) were conducted in the North East of England using a phenomenological framework and purposive sampling, informing a UK online survey (n=514).4 categories were identified as contributing to variations in the diagnosis and management of heart failure. Three previously known categories included: uncertainty about clinical practice, the value of clinical guidelines and tensions between individual and organisational practice. A new category concerned uncertainty about end-of-life care. Survey responses found that confidence varied among professional groups in diagnosing left ventricular systolic dysfunction (LVSD): 95% of cardiologists, 93% of general physicians, 66% of general practitioners (GPs) and 32% of heart failure nurses. For heart failure with preserved ejection fraction (HFpEF), confidence levels were much lower: 58% of cardiologists, 43% of general physicians, 7% of GPs and 6% of heart failure nurses. Only 5-35% of respondents used natriuretic peptides for LVSD or HFpEF. Confidence in interpreting test findings was fundamental to the use of all diagnostic tests. Clinical guidelines were reported to be helpful when diagnosing LVSD by 33% of nurses and 50-56% of other groups, but fell to 5-28% for HFpEF. Some GPs did not routinely initiate diuretics (23%), ACE-inhibitors (22%) or ?-blockers (38%) for LVSD for reasons including historical teaching, perceived side effects and burden of monitoring. For end-of-life care, there was no consensus about responsibility for heart failure management.Reported differences in the way heart failure is diagnosed and managed have changed little in the past decade. Variable access to diagnostic tests, modes of care delivery and non-uniform management approaches persist. The current National Health Service (NHS) context may not be conducive to addressing these issues.

Project description:BackgroundDiabetes mellitus (DM) is an important public health concern in Singapore and places a massive burden on health care spending. Tackling chronic diseases such as DM requires innovative strategies to integrate patients' data from diverse sources and use scientific discovery to inform clinical practice that can help better manage the disease. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) was chosen as the framework for integrating data with disparate formats.ObjectiveThe study aimed to evaluate the feasibility of converting Singapore based data source, comprising of electronic health records (EHR), cognitive and depression assessment questionnaire data to OMOP CDM standard. Additionally, we also validate whether our OMOP CDM instance is fit for the purpose of research by executing a simple treatment pathways study using Atlas, a graphical user interface tool to conduct analysis on OMOP CDM data as a proof of concept.MethodsWe used de-identified EHR, cognitive, and depression assessment questionnaires data from a tertiary care hospital in Singapore to convert it to version 5.3.1 of OMOP CDM standard. We evaluate the OMOP CDM conversion by (1) assessing the mapping coverage (that is the percentage of source terms mapped to OMOP CDM standard); (2) local raw dataset versus CDM dataset analysis; and (3) Implementing Harmonized Intrinsic Data Quality Framework using an open-source R package called Data Quality Dashboard.ResultsThe content coverage of OMOP CDM vocabularies is more than 90% for clinical data, but only around 11% for questionnaire data. The comparison of characteristics between source and target data returned consistent results and our transformed data did not pass 38 (1.4%) out of 2,622 quality checks.ConclusionAdoption of OMOP CDM at our site demonstrated that EHR data are feasible for standardization with minimal information loss, whereas challenges remain for standardizing cognitive and depression assessment questionnaire data that requires further work.

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Waste management CDM projects barriers NVivo 10® qualitative dataset.

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Waste management CDM projects barriers NVivo 10® qualitative dataset.

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