Capturing the underlying distribution in meta-analysis: Credibility and tolerance intervals.

Tolerance intervals provide a bracket intended to contain a percentage (e.g., 80%) of a population distribution given sample estimates of the mean and variance.

Chaplaincy in the outpatient setting-getting from here to there.

One of the most evident trends in US health care and health care generally in the developed world is that more and more care is shifting to outpatient settings. This change opens up substantial opportunities, and in many cases, expectations for chaplains to extend the breadth of the care they provide in any health system. However, it also brings many challenges.

Steering by their own lights: Why regulators across Europe use different indicators to measure healthcare quality.

Despite widespread faith that quality indicators are key to healthcare improvement and regulation, surprisingly little is known about what is actually measured in different countries, nor how, nor why. To address that gap, this article compares the official indicator sets--comprising some 1100 quality measures-- used by statutory hospital regulators in England, Germany, France, and the Netherlands. The findings demonstrate that those countries' regulators strike very different balances in: the dimensions of quality they assess (e.

Quasi-experimental study designs series-paper 8: identifying quasi-experimental studies to inform systematic reviews.

Objective: This article reviews the available evidence and guidance on methods to identify reports of quasi-experimental (QE) studies to inform systematic reviews of health care, public health, international development, education, crime and justice, and social welfare.

Study Design And Setting: Research, guidance, and examples of search strategies were identified by searching a range of databases, key guidance documents, selected reviews, conference proceedings, and personal communication. Current practice and research evidence were summarized.

Basics of meta-analysis: I is not an absolute measure of heterogeneity.

When we speak about heterogeneity in a meta-analysis, our intent is usually to understand the substantive implications of the heterogeneity. If an intervention yields a mean effect size of 50 points, we want to know if the effect size in different populations varies from 40 to 60, or from 10 to 90, because this speaks to the potential utility of the intervention. While there is a common belief that the I statistic provides this information, it actually does not.

Accounting for failure: risk-based regulation and the problems of ensuring healthcare quality in the NHS.

In this paper, we examine why risk-based policy instruments have failed to improve the proportionality, effectiveness, and legitimacy of healthcare quality regulation in the National Health Service (NHS) in England. Rather than trying to prevent all possible harms, risk-based approaches promise to rationalise and manage the inevitable limits of what regulation can hope to achieve by focusing regulatory standard-setting and enforcement activity on the highest priority risks, as determined through formal assessments of their probability and consequences. As such, risk-based approaches have been enthusiastically adopted by healthcare quality regulators over the last decade.

Intelligent Monitoring? Assessing the ability of the Care Quality Commission's statistical surveillance tool to predict quality and prioritise NHS hospital inspections.

Background: The Care Quality Commission (CQC) is responsible for ensuring the quality of the health and social care delivered by more than 30 000 registered providers in England. With only limited resources for conducting on-site inspections, the CQC has used statistical surveillance tools to help it identify which providers it should prioritise for inspection. In the face of planned funding cuts, the CQC plans to put more reliance on statistical surveillance tools to assess risks to quality and prioritise inspections accordingly.

Interventions to prevent misconduct and promote integrity in research and publication.

Background: Improper practices and unprofessional conduct in clinical research have been shown to waste a significant portion of healthcare funds and harm public health.

Objectives: Our objective was to evaluate the effectiveness of educational or policy interventions in research integrity or responsible conduct of research on the behaviour and attitudes of researchers in health and other research areas.

Search Methods: We searched the CENTRAL, MEDLINE, LILACS and CINAHL health research bibliographical databases, as well as the Academic Search Complete, AGRICOLA, GeoRef, PsycINFO, ERIC, SCOPUS and Web of Science databases.

Methodological and Reporting Errors in Meta-Analytic Reviews Make Other Meta-Analysts Angry: A Commentary on Ferguson (2015).

Although Ferguson's (2015, this issue) meta-analysis addresses an important topic, we have serious concerns about how it was conducted. Because there was only one coder, we have no confidence in the reliability or validity of the coded variables. Two independent raters should have coded the studies.

How to critically read ecological meta-analyses.

Meta-analysis offers ecologists a powerful tool for knowledge synthesis. Albeit a form of review, it also shares many similarities with primary empirical research. Consequently, critical reading of meta-analyses incorporates criteria from both sets of approaches particularly because ecology is a discipline that embraces heterogeneity and broad methodologies.

Publication bias in psychological science: comment on Ferguson and Brannick (2012).

It is well documented that studies reporting statistically significant results are more likely to be published than are studies reporting nonsignificant results--a phenomenon called publication bias. Publication bias in meta-analytic reviews should be identified and reduced when possible. Ferguson and Brannick (2012) argued that the inclusion of unpublished articles is ineffective and possibly counterproductive as a means of reducing publication bias in meta-analyses.

A basic introduction to fixed-effect and random-effects models for meta-analysis.

There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data.

Violent video game effects on aggression, empathy, and prosocial behavior in eastern and western countries: a meta-analytic review.

Meta-analytic procedures were used to test the effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, empathy/desensitization, and prosocial behavior. Unique features of this meta-analytic review include (a) more restrictive methodological quality inclusion criteria than in past meta-analyses; (b) cross-cultural comparisons; (c) longitudinal studies for all outcomes except physiological arousal; (d) conservative statistical controls; (e) multiple moderator analyses; and (f) sensitivity analyses. Social-cognitive models and cultural differences between Japan and Western countries were used to generate theory-based predictions.

Reasons or excuses for avoiding meta-analysis in forest plots.

Heterogeneous data are a common problem in meta-analysis. , , and show that final synthesis is possible and desirable in most cases

Effects of occupational stress management intervention programs: a meta-analysis.

A meta-analysis was conducted to determine the effectiveness of stress management interventions in occupational settings. Thirty-six experimental studies were included, representing 55 interventions. Total sample size was 2,847.

The risks of risk-based regulation: insights from the environmental policy domain.

Risk-based regulation has become increasingly popular in recent years. Proponents argue that it facilitates robust governance, contributing to efficient and effective use of regulatory resources and delivering interventions in proportion to risk. Critics contend that the challenges of operationalising risk-based governance mitigate its potential benefits.

Beyond the significance test in administrative research and policy decisions.

Purpose: To describe confidence interval (CI) analysis and show how it can be used in administrative decisions.

Organizing Construct: Statistical significance testing should be supplemented, if not replaced, by effect size (ES) estimation and confidence interval analysis. Hypothesis testing based on the statistical significance test is the dominant paradigm in statistics; however, this approach has inherent problems which can ultimately diminish the usefulness of research for operational decisions.