833 resultados para Richards, Lyn: Handling qualitative data
Resumo:
The COntext INterchange (COIN) strategy is an approach to solving the problem of interoperability of semantically heterogeneous data sources through context mediation. COIN has used its own notation and syntax for representing ontologies. More recently, the OWL Web Ontology Language is becoming established as the W3C recommended ontology language. We propose the use of the COIN strategy to solve context disparity and ontology interoperability problems in the emerging Semantic Web – both at the ontology level and at the data level. In conjunction with this, we propose a version of the COIN ontology model that uses OWL and the emerging rules interchange language, RuleML.
Resumo:
BACKGROUND: Unsafe abortions are a serious public health problem and a major human rights issue. In low-income countries, where restrictive abortion laws are common, safe abortion care is not always available to women in need. Health care providers have an important role in the provision of abortion services. However, the shortage of health care providers in low-income countries is critical and exacerbated by the unwillingness of some health care providers to provide abortion services. The aim of this study was to identify, summarise and synthesise available research addressing health care providers' perceptions of and attitudes towards induced abortions in sub-Saharan Africa and Southeast Asia. METHODS: A systematic literature search of three databases was conducted in November 2014, as well as a manual search of reference lists. The selection criteria included quantitative and qualitative research studies written in English, regardless of the year of publication, exploring health care providers' perceptions of and attitudes towards induced abortions in sub-Saharan Africa and Southeast Asia. The quality of all articles that met the inclusion criteria was assessed. The studies were critically appraised, and thematic analysis was used to synthesise the data. RESULTS: Thirty-six studies, published during 1977 and 2014, including data from 15 different countries, met the inclusion criteria. Nine key themes were identified as influencing the health care providers' attitudes towards induced abortions: 1) human rights, 2) gender, 3) religion, 4) access, 5) unpreparedness, 6) quality of life, 7) ambivalence 8) quality of care and 9) stigma and victimisation. CONCLUSIONS: Health care providers in sub-Saharan Africa and Southeast Asia have moral-, social- and gender-based reservations about induced abortion. These reservations influence attitudes towards induced abortions and subsequently affect the relationship between the health care provider and the pregnant woman who wishes to have an abortion. A values clarification exercise among abortion care providers is needed.
Resumo:
Background. Through a national policy agreement, over 167 million Euros will be invested in the Swedish National Quality Registries (NQRs) between 2012 and 2016. One of the policy agreement¿s intentions is to increase the use of NQR data for quality improvement (QI). However, the evidence is fragmented as to how the use of medical registries and the like lead to quality improvement, and little is known about non-clinical use. The aim was therefore to investigate the perspectives of Swedish politicians and administrators on quality improvement based on national registry data. Methods. Politicians and administrators from four county councils were interviewed. A qualitative content analysis guided by the Consolidated Framework for Implementation Research (CFIR) was performed. Results. The politicians and administrators perspectives on the use of NQR data for quality improvement were mainly assigned to three of the five CFIR domains. In the domain of intervention characteristics, data reliability and access in reasonable time were not considered entirely satisfactory, making it difficult for the politico-administrative leaderships to initiate, monitor, and support timely QI efforts. Still, politicians and administrators trusted the idea of using the NQRs as a base for quality improvement. In the domain of inner setting, the organizational structures were not sufficiently developed to utilize the advantages of the NQRs, and readiness for implementation appeared to be inadequate for two reasons. Firstly, the resources for data analysis and quality improvement were not considered sufficient at politico-administrative or clinical level. Secondly, deficiencies in leadership engagement at multiple levels were described and there was a lack of consensus on the politicians¿ role and level of involvement. Regarding the domain of outer setting, there was a lack of communication and cooperation between the county councils and the national NQR organizations. Conclusions. The Swedish experiences show that a government-supported national system of well-funded, well-managed, and reputable national quality registries needs favorable local politico-administrative conditions to be used for quality improvement; such conditions are not yet in place according to local politicians and administrators.
Resumo:
OBJECTIVES The purpose of the study was to provide empirical evidence about the reporting of methodology to address missing outcome data and the acknowledgement of their impact in Cochrane systematic reviews in the mental health field. METHODS Systematic reviews published in the Cochrane Database of Systematic Reviews after January 1, 2009 by three Cochrane Review Groups relating to mental health were included. RESULTS One hundred ninety systematic reviews were considered. Missing outcome data were present in at least one included study in 175 systematic reviews. Of these 175 systematic reviews, 147 (84%) accounted for missing outcome data by considering a relevant primary or secondary outcome (e.g., dropout). Missing outcome data implications were reported only in 61 (35%) systematic reviews and primarily in the discussion section by commenting on the amount of the missing outcome data. One hundred forty eligible meta-analyses with missing data were scrutinized. Seventy-nine (56%) of them had studies with total dropout rate between 10 and 30%. One hundred nine (78%) meta-analyses reported to have performed intention-to-treat analysis by including trials with imputed outcome data. Sensitivity analysis for incomplete outcome data was implemented in less than 20% of the meta-analyses. CONCLUSIONS Reporting of the techniques for handling missing outcome data and their implications in the findings of the systematic reviews are suboptimal.
Resumo:
ISSIS is the instrument for imaging and slitless spectroscopy on-board WSO-UV. In this article, a detailed comparison between ISSIS expected radiometric performance and other ultraviolet instruments is shown. In addition, we present preliminary information on the performance verification tests and on the foreseen procedures for in-flight operation and data handling.
Resumo:
Objectives To explore how general practitioners have accessed and evaluated evidence from trials on the use of statin lipid lowering drugs and incorporated this evidence into their practice. To draw out the practical implications of this study for strategies to integrate clinical evidence into general medical practice.
Resumo:
National Highway Traffic Safety Administration, Washington, D.C.
Resumo:
"RADC-TDR-63-320."
Resumo:
"Project no. 9-38-01-000 T 204, contract no. DA 44-177-TC-462."
Resumo:
Mode of access: Internet.
Resumo:
One of the main challenges of classifying clinical data is determining how to handle missing features. Most research favours imputing of missing values or neglecting records that include missing data, both of which can degrade accuracy when missing values exceed a certain level. In this research we propose a methodology to handle data sets with a large percentage of missing values and with high variability in which particular data are missing. Feature selection is effected by picking variables sequentially in order of maximum correlation with the dependent variable and minimum correlation with variables already selected. Classification models are generated individually for each test case based on its particular feature set and the matching data values available in the training population. The method was applied to real patients' anonymous mental-health data where the task was to predict the suicide risk judgement clinicians would give for each patient's data, with eleven possible outcome classes: zero to ten, representing no risk to maximum risk. The results compare favourably with alternative methods and have the advantage of ensuring explanations of risk are based only on the data given, not imputed data. This is important for clinical decision support systems using human expertise for modelling and explaining predictions.