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Purpose: This study aims to examine the effects of ethical leadership on job satisfaction, affective commitment and burnout of health care employees, considering frustration tolerance and emotional stability as moderating variables. Design/methodology/approach: A questionnaire was used to survey health care professionals working in private and public Austrian health-care organizations (hospitals, nursing homes, rehabilitation centers and sanatoriums). The questionnaire consisted of items from well-established scales. The collected data (n = 458) was analyzed using correlation and regression analyzes. Findings: Findings indicated that ethical leadership is significantly positively related to job satisfaction (r = 0.485, p < 0.01) and affective commitment (r = 0.461, p < 0.01) and is significantly negatively related to burnout (r = −0.347, p < 0.01). The results also suggest that frustration tolerance (ß = 0.101, p < 0.1) and emotional stability (ß = 0.093, p < 0.1) moderate the relationship between ethical leadership and burnout. Furthermore, a moderation effect of emotional stability in the ethical leadership and affective commitment relation was indicated. No moderation effect was found for frustration tolerance or emotional stability for the relationship between ethical leadership and job satisfaction. Practical implications: Ethical leadership emphasizes the socio-emotional dimension in a leader-employee relationship, which can easily be neglected in times of staff cuts and work overload. Leadership training should include the development of skills in how to visibly act as a moral person, as well as how to set clear ethical standards and communicate them to employees. Originality/value: This study adds value to the limited evidence on the beneficial role of ethical leadership in health care settings. In addition, frustration tolerance and emotional stability have not before been investigated as moderators. [ABSTRACT FROM AUTHOR]

Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95 % coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys. [ABSTRACT FROM AUTHOR]

Background: Adolescence and young adulthood carry risk for suicidal thoughts and behaviours (STB). An increasing subpopulation of young people consists of college students. STB prevalence estimates among college students vary widely, precluding a validated point of reference. In addition, little is known on predictors for between-study heterogeneity in STB prevalence. Methods: A systematic literature search identified 36 college student samples that were assessed for STB outcomes, representing a total of 634 662 students [median sample size = 2082 (IQR 353-5200); median response rate = 74% (IQR 37-89%)]. We used random-effects meta-analyses to obtain pooled STB prevalence estimates, and multivariate metaregression models to identify predictors of between-study heterogeneity. Results: Pooled prevalence estimates of lifetime suicidal ideation, plans, and attempts were 22.3% [95% confidence interval (CI) 19.5-25.3%], 6.1% (95% CI 4.8-7.7%), and 3.2% (95% CI 2.2-4.5%), respectively. For 12-month prevalence, this was 10.6% (95% CI 9.1-12.3%), 3.0% (95% CI 2.1-4.0%), and 1.2% (95% CI 0.8-1.6%), respectively. Measures of heterogeneity were high for all outcomes (I2 = 93.2-99.9%), indicating substantial between-study heterogeneity not due to sampling error. Pooled estimates were generally higher for females, as compared with males (risk ratios in the range 1.12-1.67). Higher STB estimates were also found in samples with lower response rates, when using broad definitions of suicidality, and in samples from Asia. Conclusions: Based on the currently available evidence, STB seem to be common among college students. Future studies should: (1) incorporate refusal conversion strategies to obtain adequate response rates, and (2) use more fine-grained measures to assess suicidal ideation. [ABSTRACT FROM AUTHOR]

Background:The use of the borderline regression method (BRM) is a widely accepted standard setting method for OSCEs. However, it is unclear whether this method is appropriate for use with small cohorts (e.g. specialist post-graduate examinations). Aims and methods:This work uses an innovative application of resampling methods applied to four pre-existing OSCE data sets (number of stations between 17 and 21) from two institutions to investigate how the robustness of the BRM changes as the cohort size varies. Using a variety of metrics, the ‘quality’ of an OSCE is evaluated for cohorts of approximatelyn = 300 down ton = 15. Estimates of the standard error in station-level and overall pass marks,R2coefficient, and Cronbach’s alpha are all calculated as cohort size varies. Results and conclusion: For larger cohorts (n > 200), the standard error in the overall pass mark is small (less than 0.5%), and for individual stations is of the order of 1–2%. These errors grow as the sample size reduces, with cohorts of less than 50 candidates showing unacceptably large standard error. Alpha andR2also become unstable for small cohorts. The resampling methodology is shown to be robust and has the potential to be more widely applied in standard setting and medical assessment quality assurance and research. [ABSTRACT FROM PUBLISHER]

The authors express their views on an article on bootstrap-based methods for statistical estimation, that is published in this issue. Topics discussed include correlation coefficients in a linear regression model; estimation of confidence intervals for adaptive bootstrap methods; and normalized estimation error in regression analysis.