995 resultados para Statutory interpretation


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Hoekstra et al. (Psychonomic Bulletin & Review, 2014, 21:1157–1164) surveyed the interpretation of confidence intervals (CIs) by first-year students, master students, and researchers with six items expressing misinterpretations of CIs. They asked respondents to answer all items, computed the number of items endorsed, and concluded that misinterpretation of CIs is robust across groups. Their design may have produced this outcome artifactually for reasons that we describe. This paper discusses first the two interpretations of CIs and, hence, why misinterpretation cannot be inferred from endorsement of some of the items. Next, a re-analysis of Hoekstra et al.’s data reveals some puzzling differences between first-year and master students that demand further investigation. For that purpose, we designed a replication study with an extended questionnaire including two additional items that express correct interpretations of CIs (to compare endorsement of correct vs. nominally incorrect interpretations) and we asked master students to indicate which items they would have omitted had they had the option (to distinguish deliberate from uninformed endorsement caused by the forced-response format). Results showed that incognizant first-year students endorsed correct and nominally incorrect items identically, revealing that the two item types are not differentially attractive superficially; in contrast, master students were distinctively more prone to endorsing correct items when their uninformed responses were removed, although they admitted to nescience more often that might have been expected. Implications for teaching practices are discussed.

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Purpose: Research suggests that nurses and nursing students lack competence in basic electrocardiogram (ECG) interpretation. Self-efficacy is considered to be paramount in the development of one's competence. The aim of this study was to develop and psychometrically evaluate a scale to assess self-efficacy of nursing students in basic ECG interpretation. Materials and methods: Observational cross-sectional study with a convenience sample of 293 nursing students. The basic ECG interpretation self-efficacy scale (ECG-SES) was developed and psychometrically tested in terms of reliability (internal consistency and temporal stability) and validity (content, criterion and construct). The ECG-SES’ internal consistency was explored by calculating the Cronbach's alpha coefficient (α); its temporal stability was investigated by calculating the Pearson correlation coefficient (r) between the participants’ results on a test–retest separated by a 4-week interval. The content validity index of the items (I-CVI) and the scale (S-CVI) was calculated based on the reviews of a panel of 16 experts. Criterion validity was explored by correlating the participants’ results on the ECG-SES with their results on the New General Self-Efficacy Scale (NGSE). 1 Construct validity was investigated by performing Principal Component Analysis (PCA) and known-group analysis. Results: The excellent reliability of the ECG-SES was evidenced by its internal consistency (α = 0.98) and its temporal stability at the 4-week re-test (r = 0.81; p < 0.01). The ECG-SES’ content validity was also excellent (all items’ I-CVI = 0.94–1; S-CVI = 0.99). A strong, significant correlation between the NGSE and the ECG-SES (r = 0.70; p < 0.01) showed its criterion validity. Corroborating the ECG-SES’ construct validity, PCA revealed that all its items loaded on a single factor that explained 74.6% of the total variance found. Furthermore, known-groups analysis showed the ECG-SES’ ability to detect expected differences in self-efficacy between groups with different training experiences (p < 0.01). Conclusion: The ECG-SES showed excellent psychometric properties for measuring the self-efficacy of nursing students in basic ECG interpretation.

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Purpose: Nurse ability to recognise patient arrhythmias could contribute to preventing in-hospital cardiac arrest. Research suggests that nurses and nursing students lack competence in electrocardiogram (ECG) interpretation. The aim of this study was to compare the effects of two training strategies on nursing students’ acquisition of competence in ECG interpretation. Materials and methods: A controlled randomised trial with 98 nursing students. Divided in groups of 12–16, participants were randomly allocated to one of the following 3-h teaching intervention groups: 1) traditional instructor-led (TILG), and 2) flipped classroom (FCG). Participants’ competence in ECG interpretation was measured in terms of knowledge (%), skills (%) and self-efficacy (%) using a specifically designed and previously validated toolkit at pre-test and post-test. Two-way MANOVA explored the interaction effect between ‘teaching group’ and ‘time of assessment’ and its impact on participants’ competence. Within-group differences at pre-test and post-test were explored by carrying out paired t-tests. Between-group differences at pre- and post-test were examined by performing independent t-test analysis. Results: There was a statistically significant interaction effect between ‘teaching group’ and ‘time of assessment’ on participants’ competence in ECG interpretation (F(3,190) = 86.541, p = 0.001; Wilks’ Λ = 0.423). At pre-test, differences in knowledge (TILG = 35.12 ± 12.07; FCG = 35.66 ± 10.66), skills (TILG = 14.05 ± 10.37; FCG = 14.82 ± 14.14), self-efficacy (TILG = 46.22 ± 23.78; FCG = 40.01 ± 21.77) and all other variables were non-significant (p > 0.05). At post-test, knowledge (TILG = 55.12 ± 14.16; FCG = 94.2 ± 7.31), skills (TILG = 36.90 ± 16.45; FCG = 86.43 ± 14.32) and self-efficacy (TILG = 70.78 ± 14.55; FCG = 79.98 ± 10.35) had significantly improved, regardless of the training received (p < 0.05). Nonetheless, participants in the FCG scored significantly higher than participants in the TILG in knowledge, skills and self-efficacy (p < 0.05). Conclusion: Flipping the classroom for teaching ECG interpretation to nursing students may be more effective than using a traditional instructor-led approach in terms of immediate acquisition of competence in terms of knowledge, skills and self-efficacy. Further research on the effects of both teaching strategies on the retention of the competence will be undertaken.

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The fundamental objective for health research is to determine whether changes should be made to clinical decisions. Decisions made by veterinary surgeons in the light of new research evidence are known to be influenced by their prior beliefs, especially their initial opinions about the plausibility of possible results. In this paper, clinical trial results for a bovine mastitis control plan were evaluated within a Bayesian context, to incorporate a community of prior distributions that represented a spectrum of clinical prior beliefs. The aim was to quantify the effect of veterinary surgeons’ initial viewpoints on the interpretation of the trial results. A Bayesian analysis was conducted using Markov chain Monte Carlo procedures. Stochastic models included a financial cost attributed to a change in clinical mastitis following implementation of the control plan. Prior distributions were incorporated that covered a realistic range of possible clinical viewpoints, including scepticism, enthusiasm and uncertainty. Posterior distributions revealed important differences in the financial gain that clinicians with different starting viewpoints would anticipate from the mastitis control plan, given the actual research results. For example, a severe sceptic would ascribe a probability of 0.50 for a return of <£5 per cow in an average herd that implemented the plan, whereas an enthusiast would ascribe this probability for a return of >£20 per cow. Simulations using increased trial sizes indicated that if the original study was four times as large, an initial sceptic would be more convinced about the efficacy of the control plan but would still anticipate less financial return than an initial enthusiast would anticipate after the original study. In conclusion, it is possible to estimate how clinicians’ prior beliefs influence their interpretation of research evidence. Further research on the extent to which different interpretations of evidence result in changes to clinical practice would be worthwhile.

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Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.