Background While medical marijuana use is legal in more than half of U.S. states, evidence is limited about the preparation of physicians-in-training to prescribe medical marijuana. We asked whether current medical school and graduate medical educational training prepare physicians to prescribe medical marijuana. Methods We conducted a national survey of U.S. medical school curriculum deans, a similar survey of residents and fellows at Washington University in St. Louis, and a query of the Association of American Medical Colleges (AAMC) Curriculum Inventory database for keywords associated with medical marijuana. Results Surveys were obtained from 101 curriculum deans, and 258 residents and fellows. 145 schools were included in the curriculum search. The majority of deans (66.7%) reported that their graduates were not at all prepared to prescribe medical marijuana, and 25.0% reported that their graduates were not at all prepared to answer questions about medical marijuana. The vast majority of residents and fellows (89.5%) felt not at all prepared to prescribe medical marijuana, while 35.3% felt not at all prepared to answer questions, and 84.9% reported receiving no education in medical school or residency on medical marijuana. Finally, only 9% of medical school curriculums document in the AAMC Curriculum Inventory database content on medical marijuana. Conclusions Our study highlights a fundamental mismatch between the state-level legalization of medical marijuana and the lack of preparation of physicians-in-training to prescribe it. With even more states on the cusp of legalizing medical marijuana, physician training should adapt to encompass this new reality of medical practice.

We investigated the potential benefits of a novel cognitive training protocol and an aerobic exercise intervention, both individually and in concert, on older adults’ performances in laboratory simulations of select real-world tasks. The cognitive training focused on a range of cognitive processes, including attentional coordination, prospective memory, and retrospective-memory retrieval, processes that are likely involved in many everyday tasks, and that decline with age. Primary outcome measures were three laboratory tasks that simulated everyday activities: Cooking Breakfast, Virtual Week, and Memory for Health Information. Two months of cognitive training improved older adults’ performance on prospective memory tasks embedded in Virtual Week. Cognitive training, either alone or in combination with six months of aerobic exercise, did not significantly improve Cooking Breakfast or Memory for Health Information. Although gains in aerobic power were comparable to previous reports, aerobic exercise did not produce improvements for the primary outcome measures. Discussion focuses on the possibility that cognitive training programs that include explicit strategy instruction and varied practice contexts may confer gains to older adults for performance on cognitively challenging everyday tasks.

Prospective memory (PM) tasks are those that must be performed in the future (e.g., attend an appointment). While these everyday tasks can be especially relevant for older adults (i.e., medication adherence), and have been associated with age-related decline, PM has been virtually overlooked in the cognitive training domain. This article describes the first comprehensive PM training intervention. Older adults (age 55 to 75) who received training completed 8 weekly PM training sessions that consisted of variable PM training tasks, strategy-focused discussion, and homework assignments. Those assigned to a control group completed only the first and last training task. On both a real-world proxy PM transfer task and the training tasks detailed here, there was a positive impact of PM training, suggesting practical benefits of the current training package for older adults. Benefits may also extend to other special populations who experience PM impairments (e.g., traumatic brain injury [TBI], Parkinson’s).

This chapter focuses on how age differences might affect other aspects of cognitive function. With respect to working memory in particular, there seemed to be multiple ways in which it might be affected by changes in processing speed, as well as multiple ways in which working memory might affect higher-order cognitive processes such as reasoning. Changes in cognitive processing speed might precipitate a cascade in which increase or decrease in speed would lead to changes in working memory function. The methodology that was adopted for studying age differences in working memory was an elegant study of domain-specificity in the working memory function of young adults. The experimental paradigm seemed to provide an excellent tool for dissecting the effects of age-related changes in working memory. In addition to age and domain, processing speed is another important source of variation in working memory.

Spearman correlation demonstrated moderate to strong correlation between self-assessments completed alone (no simultaneous peer assessment) and self-assessments completed at the time of peer assessments (ρ = .59, p < .0001) but weak correlation between the two self-assessments and peer assessments (alone: ρ = .13, p < .013; at time of peer: ρ = .21, p < .0001). Generalized estimating equation models revealed that self-assessments done alone (p < .0001) were a significant predictor of self-assessments done at the time of peer. Course was also a significant predictor (p = .01) of self-assessment scores done at the time of peer. Peer assessment score was not a significant predictor. Bhapkar's test revealed subgroup membership based on the relationship between self- and peer ratings was relatively stable across Time 1 and Time 2 assessments (χ = 0.83, p = .84) for all but one subgroup; members of the subgroup with initially high self-assessment and low peer assessment were significantly more likely to move to a new classification at the second measurement. A missing data analysis revealed that students who completed all self-assessments had significantly higher average peer assessment ratings compared to students who completed one or no self-assessments with a difference of -0.32, 95% confidence interval [-0.48, -0.15]. Insights: Multiple measurements of simultaneous self- and peer assessment identified a subgroup of students who consistently rated themselves higher on professionalism attributes relative to the low ratings given by their peers. This subgroup of preclinical students, along with those who elected to not complete self-assessments, may be at risk for professionalism concerns. Use of this multisource feedback tool to measure perceptual stability of professionalism behaviors is a new approach that may assist with early identification of at-risk students during preclinical years.