Intrapartum cardiotocograph monitoring and perinatal outcomes for women at risk: Literature review

ProblemCaesarean section rates have risen in high-income countries. One of the potential drivers for this is the widespread use of CTG monitoring.BackgroundIntrapartum cardiotocograph monitoring is considered to be indicated for women at risk for poor perinatal outcome.AimThis systematic literature review with meta-analysis examined randomised controlled trials and non-experimental research to determine whether cardiotocograph monitoring rather than intermittent auscultation during labour was associated with changes in perinatal mortality or cerebral palsy rates for high-risk women.MethodsA systematic search for research published up to 2019 was conducted using PubMed, CINAHL, Cochrane, and Web of Science databases. Non-experimental and randomised controlled trial research in populations of women at risk which compared intrapartum cardiotocography with intermittent auscultation and reported on stillbirth, neonatal mortality, perinatal mortality and/or cerebral palsy were included. Relative risks were calculated from extracted data, and meta-analysis of randomised controlled trials was undertaken.FindingsNine randomised controlled trials and 26 non-experimental studies were included. Meta-analysis of pooled data from RCTs in mixed- and high-risk populations found no statistically significant differences in perinatal mortality rates. The majority of non-experimental research was at critical risk of bias and should not be relied on to inform practice. Cardiotocograph monitoring during preterm labour was associated with a higher incidence of cerebral palsy.DiscussionResearch evidence failed to demonstrate perinatal benefits from intrapartum cardiotocograph monitoring for women at risk for poor perinatal outcome.ConclusionThere is an urgent need for well-designed research to consider whether intrapartum cardiotocograph monitoring provides benefits.     

How do I get my way? A meta-analytic review of research on influence tactics

Despite considerable research investigating the role of influence tactics on work-related outcomes in organizations, consensus on the effectiveness of influence tactics has been elusive. Specifically, there is a lack of integration concerning the relationships between proactive influence tactics and their outcomes. We investigate the effectiveness of 11 influence tactics from a comprehensive perspective using meta-analytic techniques. In particular, the current study focuses on relationships between each of the 11 influence tactics (i.e., rational persuasion, exchange, inspirational appeal, legitimating, apprising, pressure, collaboration, ingratiation, consultation, personal appeals, and coalition) and task- and relations-oriented outcomes. In addition, we investigate the moderating effects of the direction of influence tactics, measurement of influence tactics, singular influence tactic (versus use of a combination of influence tactics), independence of data sources, and study setting involved in the study. Regardless of task- and relations-oriented outcomes, based on 49 independent samples (N = 8987), our results show positive relationships between outcomes and rational persuasion, inspirational appeal, apprising, collaboration, ingratiation, consultation, and a negative relationship between pressure and outcomes. Rational persuasion is the only tactic which held stable positive relationships with both categories of outcomes regardless of moderating factors. Implications and directions for future research in the area of influence tactics are discussed.     

The stressor–strain relationship in diary studies: A meta-analysis of the within and between levels

Daily diary studies use the same set of measures repeatedly for several days. Within the work stress domain, these studies are able to isolate the effects of daily exposure to stressors within people from the general level of stressors between people. This meta-analysis investigated both content-related and methodological aspects of workplace stressor–strain relationships in diary studies. Results from 55 unique samples (a combined sample size of 5409) indicated that the magnitude of the stressor–strain relationship was stronger at the between-person level than the within-person level. Further, when the stressor was measured prior to the strain (within the same day), the relationship was somewhat stronger than when stressor and strain were measured concurrently. This suggests that stressor–strain effects might take some time to fully manifest. Differences were also detected among types of strains: affective strains had stronger relationship with stressors than behavioural strains. There were also differences in the stressor–strain relationship depending on both the type of strain and the timing of their respective measurement (concurrent versus predictive), suggesting that certain strain responses require more time to manifest. Overall, this meta-analysis elucidates important considerations in the design and interpretation of diary studies on occupational stress.     

Confidence intervals for treatment effect from restricted maximum likelihood

An explicit form of confidence intervals for the treatment effect in random effects meta-analysis model obtained from Harville–Jeske–Kenward–Roger approach is given. These restricted likelihood based intervals are compared to alternative procedures commonly used in collaborative studies when the number of participants is small and study-specific variances are heterogeneous. Monte Carlo simulation experiments show that the former intervals have quite conservative coverage probabilities and favor the latter intervals.     

知识视角下组织绩效影响因素的元分析与结构方程研究

国内外对知识分享、组织学习与组织绩效的关系已经做了相当程度的研究,然而国内尚缺乏系统的总结和评价.文章以2000至2013年间国内外研究文献为样本,以元分析与路径分析相结合的方法探讨知识分享、组织学习与组织绩效的关系.结果表明三者间效应值具有显著正相关,调节变量分析的结果表明文献来源区域是知识分享与组织绩效效应量间产生异质性的原因,在此基础上提出了相关启示.     

A re-evaluation of random-effects meta-analysis

Summary.  Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.     

Small sample distribution of the likelihood ratio test in the random effects model

In this paper we examine the small sample distribution of the likelihood ratio test in the random effects model which is often recommended for meta-analyses. We find that this distribution depends strongly on the true value of the heterogeneity parameter (between-study variance) of the model, and that the correct p-value may be quite different from its large sample approximation. We recommend that the dependence of the heterogeneity parameter be examined for the data at hand and suggest a (simulation) method for this. Our setup allows for explanatory variables on the study level (meta-regression) and we discuss other possible applications, too. Two data sets are analyzed and two simulation studies are performed for illustration.     

Alienation and its correlates: A meta-analysis

We provide a meta-analysis of alienation, outlining the extent to which it is predicted by individual differences (need for achievement), role stressors (role conflict), leader dimensions (initiating structure), and aspects of the work context (formalization). We also examine its relationship with outcomes such as employee attitudes (job satisfaction), performance (task performance), withdrawal (absenteeism), and side effects (drinking). We examined these relationships based on data from 45 primary studies and 227 statistically independent relationships. Our meta-analysis provides cumulative evidence for effect sizes across multiple settings and respondents, clarifies ambiguous aspects of the construct, and presents more information on the extent to which alienation can be seen as the opposite of job involvement.     

A modified random-effect procedure for combining risk difference in sets of 2×2 tables from clinical trials

Summary Meta-analyses of sets of clinical trials often combine risk differences from several 2×2 tables according to a random-effects model. The DerSimonian-Laird random-effects procedure, widely used for estimating the populaton mean risk difference, weights the risk difference from each primary study inversely proportional to an estimate of its variance (the sum of the between-study variance and the conditional within-study variance). Because those weights are not independent of the risk differences, however, the procedure sometimes exhibits bias and unnatural behavior. The present paper proposes a modified weighting scheme that uses the unconditional within-study variance to avoid this source of bias. The modified procedure has variance closer to that available from weighting by ideal weights when such weights are known. We studied the modified procedure in extensive simulation experiments using situations whose parameters resemble those of actual studies in medical research. For comparison we also included two unbiased procedures, the unweighted mean and a sample-size-weighted mean; their relative variability depends on the extent of heterogeneity among the primary studies. An example illustrates the application of the procedures to actual data and the differences among the results. This research was supported by Grant HS 05936 from the Agency for Health Care Policy and Research to Harvard University.