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Originally put forward to defend history from the encroachment of physics, the distinction between understanding and explanation was built into the foundations of Karl Jaspers’ ‘phenomenological’ psychiatry, and it is revised, used and defended by many still working in that tradition. On the face of it, this is rather curious. I examine what this notion of ‘understanding’ amounts to, why it entered and remains influential in psychiatry, and what insights for contemporary psychiatry are buried in the notion. I argue that it is unhelpfully associated with the view that the mental is epistemologically and methodologically autonomous, but that it nevertheless highlights an important lacuna in many views of psychiatry and the scientific study of humans more generally.

Biomathematical models (BMMs) are parametric models that quantitatively predict fatigue and are routinely implemented in fatigue risk management systems in increasingly diverse workplaces. There have been consistent calls for an improved "next generation” of BMMs that provide more accurate and targeted predictions of human fatigue. This review examines the core characteristics of next-generation advancements in BMMs, including tailoring with field data, individual-level parameter tuning and real-time fatigue prediction, extensions to account for additional factors that influence fatigue, and emerging nonparametric methodologies that may augment or provide alternatives to BMMs. Examination of past literature and quantitative examples suggests there are notable challenges to advancing BMMs beyond their current applications. Adoption of multi-model frameworks, including quantitative joint modelling and machine learning, was identified as crucial to next-generation models. We close with general recommendations for researchers and model developers, including focusing research efforts on understanding the cognitive dynamics underpinning fatigue-related vigilance decrements, applying emerging dynamic modelling methods to fatigue data from field settings, and improving the adoption of open scientific practices in fatigue research.

Background:There is discontent and turnover among faculty at US academic health centers because of the challenges in balancing clinical, research, teaching, and work–life responsibilities in the current healthcare environment. One potential strategy to improve faculty satisfaction and limit turnover is through faculty mentoring programs.Methods:A Mentor Leadership Council was formed to design and implement an institution-wide faculty mentoring program across all colleges at an academic health center. The authors conducted an experimental study of the impact of the mentoring program using pre-intervention (2011) and 6-year (2017) post-intervention faculty surveys that measured the long-term effectiveness of the program.Results:The percent of faculty who responded to the surveys was 45.9% (656/1428) in 2011 and 40.2% (706/1756) in 2017. For faculty below the rank of full professor, percent of faculty with a mentor (45.3% vs. 67.1%, P < 0.001), familiarity with promotion criteria (81.7% vs. 90.0%, P = 0.001), and satisfaction with department’s support of career (75.6% vs. 84.7%, P = 0.002) improved. The percent of full professors serving as mentors also increased from 50.3% in 2011 to 68.0% in 2017 (P = 0.002). However, the percent of non-retiring faculty considering leaving the institution over the next 2 years increased from 18.8% in 2011 to 24.3% in 2017 (P = 0.02).Conclusions:Implementation of an institution-wide faculty mentoring program significantly improved metrics of career development and faculty satisfaction but was not associated with a reduction in the percent of faculty considering leaving the institution. This suggests the need for additional efforts to identify and limit factors driving faculty turnover.

Lee et al. (2019) make several practical recommendations for replicable and useful cognitive modeling. They also point out that the ultimate test of the usefulness of a cognitive model is its ability to solve practical problems. Solution-oriented modeling requires engaging practitioners who understand the relevant applied domain but may lack extensive modeling expertise. In this commentary, we argue that for cognitive modeling to reach practitioners there is a pressing need to move beyond providing the bare minimum information required for reproducibility, and instead aim for an improved standard of transparency and reproducibility in cognitive modeling research. We discuss several mechanisms by which reproducible research can foster engagement with applied practitioners. Notably, reproducible materials provide a starting point for practitioners to experiment with cognitive models and evaluate whether they are suitable for their domain of expertise. This is essential because solving complex problems requires exploring a range of modeling approaches, and there may not be time to implement each possible approach from the ground up. Several specific recommendations for best practice are provided, including the application of containerization technologies. We also note the broader benefits of adopting gold standard reproducible practices within the field.