In personnel- and educational selection, a substantial gap exists between research and practice, since evidence-based assessment instruments and decision-making procedures are underutilized. We provide an overview of studies that investigated interventions to encourage the use of evidence-based assessment methods, or factors related to their use. The most promising studies were grounded in self-determination theory. Training and autonomy in the design of evidence-based assessment methods were positively related to their use, while negative stakeholder perceptions decreased practitioners’ intentions to use evidence-based assessment methods. Use of evidence-based decision-making procedures was positively related to access to such procedures, information to use it, and autonomy over the procedure, but negatively related to receiving outcome feedback. A review of the professional selection literature showed that the implementation of evidence-based assessment was hardly discussed. We conclude with an agenda for future research on encouraging evidence-based assessment practice.

In decision-making, it is important not only to use the correct information but also to combine information in an optimal way. There are robust research findings that a mechanical combination of information for personnel and educational selection matches or outperforms a holistic combination of information. However, practitioners and policy makers seldom use mechanical combination for decision-making. One of the important conditions for scientific results to be used in practice and to be part of policymaking is that results are easily accessible. To increase the accessibility of mechanical judgment prediction procedures, we (1) explain in detail how mechanical combination procedures work, ( 2) provide examples to illustrate these procedures, and (3) discuss some limitations of mechanical decision-making.

Context. We present a new catalogue of the high-mass X-ray binaries (HMXBs) in the Galaxy improving upon the most recent such catalogue. We include new HMXBs discovered since aforementioned publication and revise the classification for several objects previously considered HMXBs or candidates. The catalogue includes both basic information such as source names, coordinates, types, and more detailed data such as distance and X-ray luminosity estimates, binary system parameters and other characteristic properties of 169 HMXBs, together with appropriate references to the literature. Finding charts in several bands from infra-red to hard X-rays are also included for each object. Aims. The aim of this catalogue is to provide the reader a list of all currently known Galactic HMXBs with some basic information on both compact objects and non-degenerate counterpart properties (where available). We also include objects tentatively classified as HXMBs in the literature and give a brief motivation for the classifcation in each relevant case. Methods. The catalogue is compiled based on a search of known HMXBs and candidates in all commonly available databases and literature published before 31 October 2022. Relevant properties in the optical and other bands were collected for all objects either from the literature or using the data provided by large-scale surveys. In the later case, the counterparts in each individual survey were found by cross-correlating positions of identified HMXBs with relevant databases.Results. An up-to date catalogue of Galactic HMXBs is presented to facilitate research in this area. An attempt was made to collect a larger set of relevant HMXB properties in a more uniform way compared to previously published works.

A robust finding in psychological research is that combining information with a mechanical rule results in more valid predictions than combining information holistically in the mind. Nevertheless, information is typically combined holistically in practice, resulting in suboptimal predictions and decisions. Earlier research showed that decision makers are more likely to use mechanical prediction procedures when they retain autonomy in the decision-making process. However, it remains largely unknown how different autonomy-enhancing features affect predictive validity. Therefore, in two preregistered studies (total N = 342), we investigated if and how prediction procedures can be designed such that they satisfy decision makers' autonomy needs and acceptance without reducing predictive validity. Based on archival

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In a preregistered prediction-task experiment, we investigated the effect of narrative versus quantified information on decision-maker perceptions, confidence, predictor weighting, and predictive accuracy when making performance predictions. We also investigated the effect of who quantifies information (the decision maker or someone else).As expected, we found higher perceived informativeness and use intentions for narrative than quantified information. Information presented narratively was also weighted somewhat more heavily than quantified information. Using quantitative information quantified by decision makers themselves yielded higher perceived autonomy and use intentions than quantitative information quantified by someone else. However, no differences in prediction confidence were found and self-and other-produced quantifications received identical weight. Moreover, unexpectedly, differences in weighting did not translate to differences in predictive accuracy.

Although mechanical combination results in more valid judgments and decisions than holistic combination, existing publications suggest that mechanical combination is rarely used in practice. Yet, these publications are either descriptions of anecdotal experiences or outdated surveys. Therefore, in several Western countries, we conducted two surveys (total N = 323) and two focus groups to investigate (1) how decision makers in psychological and HR practice combine information, (2) why they do (not) use mechanical combination, and (3) what may be needed to increase its use in practice. Many participants reported mostly using holistic combination, usually in teams. The most common reasons for not using mechanical combination were that algorithms are unavailable in practice and that stakeholders do not accept their use. Furthermore, decision makers do not quantify information, do not believe in research findings on evidence-based decision making, and think that combining holistic and mechanical combination results in the best decisions. The most important reason why mechanical combination is used was to increase predictive validity. To stimulate the use of mechanical combination in practice, our results suggest that decision makers should receive more training on evidence-based decision making, and decision aids supporting the use of mechanical combination should be developed.

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