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An applied study using convenience data was conducted to compare the experiences of neurodivergent adults undergoing workplace coaching before and during the pandemic.

The naturally occurring opportunity permitted a comparison of face-to-face and remote coaching in three cohorts, pre-pandemic (100% face-to-face), forced-remote (100% remote) and choice (remote or face-to-face; 85% selected remote). A total of 409 participants self-reported performance before and 12 weeks after completing an average of 11-h coaching.

Significant differences between before and after scores for performance, with large effect sizes, were reported for all three cohorts across six dependent variables: memory, time management, organisational skills, stress management, understanding neurodiversity and concentration. There was no significant difference between the cohorts in terms of the magnitude of the effect. There were significant differences between the cohorts in terms of which topics were chosen as foci for the coaching, with executive functions related topics becoming less popular in the choice cohort.

The authors abductively reasoned the results to suggest a positive relationship between personalised environments and cognitive demands for this client group. They call for further, theoretically grounded research exploring the role of coaching and environment in understanding the work performance of neurodivergent adults at work.

The study contributes to the emerging knowledge on the different experiences of in-person and video-mediated coaching. The focus on neurodivergent employees, which are heretofore less well researched within the workplace, provides essential data to support practitioners in maximising opportunity for a marginalised group.

The aims of the paper were to highlight the dearth of applied practitioner research concerning the expression of neurodiversity at work and develop an epistemological framework for a future research agenda.

A systematic empty review protocol was employed, with three a priori research questions, inquiring as to the extent of neurodiversity research within mainstream work psychology, psychology in general and lastly within cross-disciplinary academic research. The results of the final search were quality checked and categorized to illustrate where studies relevant to practice are currently located.

The academic literature was found to be lacking in contextualized, practical advice for employers or employees. The location and foci of extracted studies highlighted a growing science-practitioner gap.

The research focused on common neurominority conditions such as autism and dyslexia; it is acknowledged that the neurodiversity definition itself is broader and more anthropological in nature. A need for a comprehensive research agenda is articulated, and research questions and frameworks are proposed.

Guidance is given on applying disability accommodation to both individual and organizational targets.

The disability employment gap is unchanged since legislation was introduced. The neurodiversity concept is no longer new, and it is time for multi-disciplinary collaborations across science and practice to address the questions raised in this paper.

This paper offers an original analysis of the neurodiversity paradox, combining systematic inquiry with a narrative synthesis of the extant literature. The conceptual clarification offers clear directions for researchers and practitioners.

The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods.

The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model.

Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts.

The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians.

The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases.

The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable.

These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.