Sentence examples for bias intervention from inspiring English sources

With regard to studies that did not contain a randomized controlled design, the following aspects were evaluated: definition of study population, selection bias, intervention description, outcome definition, blinded outcome assessments, completeness of the dataset/follow-up, loss to follow, confounders, and a judgment of the validity and applicability of the study.

We found no significant subgroup heterogeneity regarding UGI bleeding, all-cause mortality, or nosocomial pneumonia when we compared trials with higher (high) versus lower (low/unclear) risk of bias, intervention drugs (PPIs vs. H2RAs), comparator types (placebo vs. no prophylaxis), presence of nutrition versus no enteral nutrition, diagnosis (TBI vs. ICH), and study location (Asia vs. non-Asian).

As the factors contributing to weight stigma become clearer, effective ways to target such factors to reduce weight stigmatising behaviour should be investigated and implemented, both in clinical settings and in medical and midwifery university training programs (see [ 22] for an example of an anti-weight bias intervention developed for health professionals in training).

This may bias intervention selection towards ones that are more likely to do this – essentially individually-based interventions – rather than more diffuse social-norm change models such as SHARE or SASA! in Uganda or COMBAT developed by the Gender Centre in Accra, Ghana, which only expect to achieve long-term goals after multi-year work in a community (Abramsky et al. 2012; Wagman et al. 2015).

This meta-analysis evaluated the impact of weight bias interventions on weight-biased attitudes and beliefs and explored potential moderators.

Though the effectiveness of weight bias interventions has been questioned, to our knowledge no meta-analysis of these interventions has been conducted.

Results reveal a small, positive effect of weight bias interventions on weight-biased attitudes and beliefs and provide useful information for future interventions.

A received wisdom is that automated decision-making serves as an anti-bias intervention.

With a case study of the algorithmic capture of hiring as heuristic device, this Article provides a taxonomy of problematic features associated with algorithmic decision-making as anti-bias intervention and argues that those features are at odds with the fundamental principle of equal opportunity in employment.

Of course, describing limitations is only the first step in designing effective interventions for minimising nurses' cognitive biases – interventions that as yet are largely unevaluated.

30 A funnel plot will be used to investigate the risk of publication bias by intervention type, provided 10 or more studies are included in the analysis for each intervention type.