Previous research shows that automatic tendency to approach alcohol plays a causal role in problematic alcohol use and can be retrained by Approach Bias Modification (ApBM). ApBM has been shown to be effective for patients diagnosed with alcohol use disorder (AUD) in inpatient treatment. This study aimed to investigate the effectiveness of adding an online ApBM to treatment as usual (TAU) in an outpatient setting compared to receiving TAU with an online placebo training. 139 AUD patients receiving face-to-face or online treatment as usual (TAU) participated in the study. The patients were randomized to an active or placebo version of 8 sessions of online ApBM over a 5-week period. The weekly consumed standard units of alcohol (primary outcome) was measured at pre-and post-training, 3 and 6 months follow-up. Approach tendency was measured pre-and-post ApBM training. No additional effect of ApBM was found on alcohol intake, nor other outcomes such as craving, depression, anxiety, or stress. A significant reduction of the alcohol approach bias was found. This research showed that approach bias retraining in AUD patients in an outpatient treatment setting reduces the tendency to approach alcohol, but this training effect does not translate into a significant difference in alcohol reduction between groups. Explanations for the lack of effects of ApBM on alcohol consumption are treatment goal and severity of AUD. Future ApBM research should target outpatients with an abstinence goal and offer alternative, more user-friendly modes of delivering ApBM training.
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Over the past 10 years, different types of financing have become available in the Netherlands. It is now possible to combine bank loans, crowdfunding loans and risk capital. Moreover, fintech applications lower the threshold for applications and reduce response times from weeks to just days or even hours. Fraser, Bhaumik and Wright (2015) point out there is a lack of knowledge of the cognitive process involved in selecting SME financing. This paper looks into the selection process financial advisers use, against the backdrop of the growing range of funding possibilities. To assess this process, we try to understand dominant habits and related heuristics. Within our explorative study, 19 experienced and independent SME financial advisers were interviewed. The questions address their knowledge, skills, experiences and choices in the selection process on the financing or refinancing of working capital and growth. Taking a grounded theoretical approach, we use Atlas TI to label all answers and statements step by step. The findings suggest a strong bias of decision-making towards the more traditional banking products. Yet advisers state they are aware of, and familiar with, other solutions. We have also found that fintech solutions are hardly used to prepare financing solutions up front. Financial advisers estimate the likelihood of acceptance by a few financial providers they know well within their personal network. We suggest that there is a behavioural approach to financing in the day-to-day decisions made by financial advisers. As long as automated selections are not fully transparent and are unable to combine all types of financing up front, financial advisers will be guided by habit or by availability, confirmation and affect heuristics, rather than looking for new financing solutions and combinations.
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BackgroundConfounding bias is a common concern in epidemiological research. Its presence is often determined by comparing exposure effects between univariable- and multivariable regression models, using an arbitrary threshold of a 10% difference to indicate confounding bias. However, many clinical researchers are not aware that the use of this change-in-estimate criterion may lead to wrong conclusions when applied to logistic regression coefficients. This is due to a statistical phenomenon called noncollapsibility, which manifests itself in logistic regression models. This paper aims to clarify the role of noncollapsibility in logistic regression and to provide guidance in determining the presence of confounding bias.MethodsA Monte Carlo simulation study was designed to uncover patterns of confounding bias and noncollapsibility effects in logistic regression. An empirical data example was used to illustrate the inability of the change-in-estimate criterion to distinguish confounding bias from noncollapsibility effects.ResultsThe simulation study showed that, depending on the sign and magnitude of the confounding bias and the noncollapsibility effect, the difference between the effect estimates from univariable- and multivariable regression models may underestimate or overestimate the magnitude of the confounding bias. Because of the noncollapsibility effect, multivariable regression analysis and inverse probability weighting provided different but valid estimates of the confounder-adjusted exposure effect. In our data example, confounding bias was underestimated by the change in estimate due to the presence of a noncollapsibility effect.ConclusionIn logistic regression, the difference between the univariable- and multivariable effect estimate might not only reflect confounding bias but also a noncollapsibility effect. Ideally, the set of confounders is determined at the study design phase and based on subject matter knowledge. To quantify confounding bias, one could compare the unadjusted exposure effect estimate and the estimate from an inverse probability weighted model.
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Nxus is a Software as a Service startup that provides higher educational institutions with one integrated community and bias-free career platform. With this platform, Nxus connects employers, students and alumni in a unique and innovative way. The Nxus platform is already deployed university-wide by the launching partner, Radboud University. The Take-Off HBO feasibility grant allows Nxus to research the feasibility of further developing the existing platform to meet the needs of Secondary Vocational Institutions (MBO-instellingen). Nxus hereby aims to address 2 present-day societal issues, the shortage of internships for MBO students and internship discrimination in the recruitment and selection process.
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