Measuring nursing competencies in the operating theatre: instrument development and psychometric analysis using Item Response Theory

BACKGROUND: Concern about the process of identifying underlying competencies that contribute to effective nursing performance has been debated with a lack of consensus surrounding an approved measurement instrument for assessing clinical performance. Although a number of methodologies are noted in the development of competency-based assessment measures, these studies are not without criticism. RESEARCH AIM: The primary aim of the study was to develop and validate a Performance Based Scoring Rubric, which included both analytical and holistic scales. The aim included examining the validity and reliability of the rubric, which was designed to measure clinical competencies in the operating theatre. RESEARCH METHOD: The fieldwork observations of 32 nurse educators and preceptors assessing the performance of 95 instrument nurses in the operating theatre were used in the calibration of the rubric. The Rasch model, a particular model among Item Response Models, was used in the calibration of each item in the rubric in an attempt at improving the measurement properties of the scale. This is done by establishing the 'fit' of the data to the conditions demanded by the Rasch model. RESULTS: Acceptable reliability estimates, specifically a high Cronbach's alpha reliability coefficient (0.940), as well as empirical support for construct and criterion validity for the rubric were achieved. Calibration of the Performance Based Scoring Rubric using Rasch model revealed that the fit statistics for most items were acceptable. CONCLUSION: The use of the Rasch model offers a number of features in developing and refining healthcare competency-based assessments, improving confidence in measuring clinical performance. The Rasch model was shown to be useful in developing and validating a competency-based assessment for measuring the competence of the instrument nurse in the operating theatre with implications for use in other areas of nursing practice.

Identification of novel therapeutics for complex diseases from genome-wide association data

Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.

Novel therapeutics for complex diseases from genome-wide association data

The development of novel therapies is essential to lower the burden of complex diseases. The purpose of this study is to identify novel therapeutics for complex diseases using bioinformatic methods. Bioinformatic tools such as candidate gene prediction tools allow identification of disease genes by identifying the potential candidate genes linked to genetic markers of the disease. Candidate gene prediction tools can only identify candidates for further research, and do not identify disease genes directly. Integration of drug-target datasets with candidate gene data-sets can identify novel potential therapeutics suitable for repositioning in clinical trials. Drug repositioning can save valuable time and money spent in therapeutic development of complex diseases.

Obesity and diabetes gene discovery approaches

New treatments are currently required for the common metabolic diseases obesity and type 2 diabetes. The identification of physiological and  biochemical factors that underlie the metabolic disturbances observed in obesity and type 2 diabetes is a key step in developing better therapeutic outcomes. The discovery of new genes and pathways involved in the  pathogenesis of these diseases is critical to this process, however  identification of genes that contribute to the risk of developing these diseases represents a significant challenge as obesity and type 2 diabetes are complex diseases with many genetic and environmental causes. A number of diverse approaches have been used to discover and validate potential new targets for obesity and diabetes. To date, DNA-based approaches using candidate gene and genome-wide linkage analysis have had limited success in identifying genomic regions or genes involved in the development of these diseases. Recent advances in the ability to evaluate linkage analysis data from large family pedigrees using variance components based linkage analysis show great promise in robustly identifying genomic regions associated with the development of obesity and diabetes. RNA-based technologies such as cDNA microarrays have identified many genes differentially expressed in tissues of healthy and diseased subjects. Using a combined approach, we are endeavouring to focus attention on differentially expressed genes located in chromosomal regions previously linked with obesity and / or diabetes. Using this strategy, we have identified Beacon as a potential new target for obesity and diabetes.

Analysis of genome-wide association study data using the protein knowledge base

Article 98

Cross-cultural adaption of the Health Educaiton Impact Questionnaire: experimental study showed expert committee, not back-translation, added value

Objectives
To assess the contribution of back-translation and expert committee to the content and psychometric properties of a translated multidimensional questionnaire.

Study Design and Setting
Recommendations for questionnaire translation include back-translation and expert committee, but their contribution to measurement properties is unknown. Four English to French translations of the Health Education Impact Questionnaire were generated with and without committee or back-translation. Face validity, acceptability, and structural properties were compared after random assignment to people with rheumatoid arthritis (N = 1,168), chronic renal failure (N = 2,368), and diabetes (N = 538). For face validity, 15 bilingual people compared translations quality with the original. Psychometric properties were examined using confirmatory factor analysis (metric and scalar invariance) and item response theory.

Results
Qualitatively, there were five types of translation errors: style, intensity, frequency/time frame, breadth, and meaning. Bilingual assessors ranked best the translations with committee (P = 0.0026). All translations had good structural properties (root mean square error of approximation <0.05; comparative fit index [CFI], ≥0.899; and Tucker–Lewis index, ≥0.889). Full measurement invariance was observed between translations (ΔCFI ≤ 0.01) with metric invariance between translations and original (lowest ΔCFI = 0.022 between fully constrained models and models with free intercepts). Item characteristic curve analyses revealed no significant differences.

Conclusion
This is the first experimental evidence that back-translation has moderate impact, whereas expert committee helps to ensure accurate content.

A defection analysis of lapsed season ticket holders : a consumer and organizational study

This paper reports on a four-stage study that investigated the motivations and future intentions of recently lapsed members (season-ticket holders) of professional sports organizations, and the marketing responses of those organizations to the issue of customer retention. Survey responses from over 1,000 recently lapsed members across five large sporting clubs were collected. A further study on one club was then conducted to compare attitudes and demographics of lapsed members to those who renewed. The responses suggests that although these lapsed members report that they joined to financially support and feel more involved with the club, they let their membership lapse primarily due to an inability to attend games. Despite joining for intangible, altruistic reasons, it seems that if these members could not get to games, they believed that the membership was not worth maintaining. These members were satisfied with the membership; however, measures of overall level of satisfaction had only a weak positive relationship with the likelihood of members rejoining in the future. These sport organizations were greatly concerned by lapsed members but have only ad hoc relationship marketing strategies in place to either prevent members lapsing or renew lapsed members.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature.
2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable.
3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation.
4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation.
5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon.
6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.

Design and analysis of a fuzzy controller for a servo system

This paper presents design and simulation investigation of a fuzzy controller and a conventional PID controller for a servo system. First, a servo system is considered and its stability is discussed. Then, a PID controller that is tuned by the Ziegler-Nichols method is formulated for controlling the servo system. To improve the servo system's dynamic response parameters, a fuzzy controller is then proposed for controlling the system. A performance comparison between the fuzzy and the PID controllers are carried out. The results are presented and discussed.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature.

2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable.

3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation.

4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation.

5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon.

6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.

Profiles of language development in pre-school children : a longitudinal latent class analysis of data from the early language in Victoria study

Background Pre-school language impairment is common and greatly reduces educational performance. Population attempts to identify children who would benefit from appropriately timed intervention might be improved by greater knowledge about the typical profiles of language development. Specifically, this could be used to help with the early identification of children who will be impaired on school entry.

Methods This study applied longitudinal latent class analysis to assessments at 8, 12, 24, 36 and 48 months on 1113 children from a population-based study, in order to identify classes exhibiting distinct communicative developmental profiles.

Results Five substantive classes were identified: Typical, i.e. development in the typical range at each age; Precocious (late), i.e. typical development in infancy followed by high probabilities of precocity from 24 months onwards; Impaired (early), i.e. high probabilities of impairment up to 12 months followed by typical language development thereafter; Impaired (late), i.e. typical development in infancy but impairment from 24 months onwards; Precocious (early), i.e. high probabilities of precocity in early life followed by typical language by 48 months. The entropy statistic (0.84) suggested classes were fairly well defined, although there was a non-trivial degree of uncertainty in classification of children. That half of the Impaired (late) class was expected to have typical language at 4 years and 6% of the numerically large Typical class was expected to be impaired at 4 years illustrates this. Characteristics indicative of social advantage were more commonly found in the classes with improving profiles.

Conclusions Developmental profiles show that some pre-schoolers' language is characterized by periods of accelerated development, slow development and catch-up growth. Given the uncertainty in classifying children into these profiles, use of this knowledge for identifying children who will be impaired on school entry is not straightforward. The findings do, however, indicate greater need for language enrichment programmes among disadvantaged children.