Systematic review of distributed practice and retrieval practice in health professions education

To determine the effect of distributed practice (spacing out of study over time) and retrieval practice (recalling information from memory) on academic grades in health professions education and to summarise a range of interventional variables that may affect study outcomes. A systematic search of seven databases in November 2022 which were screened according to predefined inclusion criteria. The Medical Education Research Study Quality Instrument (MERSQI) and Newcastle-Ottawa Scale-Education (NOS-E) were used to critically appraise eligible articles. A summary of interventional variables includes article content type, strategy type, assessment type and delay and statistical significance. Of 1818 records retrieved, 56 were eligible for inclusion and included a total of 63 experiments. Of these studies, 43 demonstrated significant benefits of distributed practice and/or retrieval practice over control and comparison groups. Included studies averaged 12.23 out of 18 on the MERSQI and averaged 4.55 out of 6 on the NOS-E. Study designs were heterogeneous with a variety of interventions, comparison groups and assessment types. Distributed practice and retrieval practice are effective at improving academic grades in health professions education. Future study quality can be improved by validating the assessment instruments, to demonstrate the reliability of outcome measures. Increasing the number of institutions included in future studies may improve the diversity of represented study participants and may enhance study quality. Future studies should consider measuring and reporting time on task which may clarify the effectiveness of distributed practice and retrieval practice. The stakes of the assessments, which may affect student motivation and therefore outcomes, should also be considered.

Using Convolutional Neural Networks for the Assessment Research of Mental Health

Existing mental health assessment methods mainly rely on experts' experience, which has subjective bias, so convolutional neural networks are applied to mental health assessment to achieve the fusion of face, voice, and gait. Among them, the OpenPose algorithm is used to extract facial and posture features; openSMILE is used to extract voice features; and attention mechanism is introduced to reasonably allocate the weight values of different modal features. As can be seen, the effective identification and evaluation of 10 indicators such as mental health somatization, depression, and anxiety are realized. Simulation results show that the proposed method can accurately assess mental health. Here, the overall recognition accuracy can reach 77.20%, and the F1 value can reach 0.77. Compared with the recognition methods based on face single-mode fusion, face + voice dual-mode fusion, and face + voice + gait multimodal fusion, the recognition accuracy and F1 value of proposed method are improved to varying degrees, and the recognition effect is better, which has certain practical application value.