At the "MMR Open Forum" of the annual Japanese Society for Mixed Methods Research meeting held in October 2023, Drs. Hisako Kakai (Aoyama Gakuin University), Taichi Hatta (Shizuoka Graduate University of Public Health), and Mitsuyuki Inaba (Ritsumeikan University) discussed mixed methods research (MMR), introducing the just published Dr. Cheryl Poth's The SAGE Handbook of Mixed Methods Design (2023), and discussed the current and future of MMR. These three leading MMR researchers in Japan introduced not only the contents of the handbook but also the process leading to its publication, showing how tits appreciative attitudes toward diversity are emphasized and embodied in it. Luckily, the author participated as a discussion participant at the forum. As I listened to the three speakers, I realized that while "diversity" is undoubtedly a vital keyword that characterizes modern society's future, it also applies to the future of MMR.

In this issue, AMMR features the three researchers' ideas of the (present and) future landscape of MMR, as they discussed at the forum. There is no doubt that the handbook itself provides a very rich learning experience, but learning about the perspectives of these three researchers will enrich our learning even more by reading the handbook.

OBJECTIVE: This study aimed to obtain multifaceted insights into the learning needs of nursing researchers from the perspective of their self-assessment (perception) of explanatory and adaptive skills related to the practice of mixed methods research (MMR).

METHODS: This study was a convergent design MMR. Attributional characteristics of three groups (high, medium, and low) based on self-assessment of MMR skills were identified using the Guetterman scale as quantitative data analysis. Next, qualitative data analysis was conducted by reading the verbatim transcripts of the focus group interviews (FGI) and coding their content according to meaning. Then, quantitative data transformations were performed on these coding results, which allowed us to identify differences in trends among the three groups. Finally, the results of quantitative and qualitative data analysis were integrated, and meta-inferences were derived to identify learning needs by the three self-assessment groups for MMR skills.

RESULTS AND CONCLUSIONS: We highlighted that the learning needs of the high, medium, and low groups differed based on their overall research skills, coupled with differences in learning environments based on their overall research characteristics and the temporal changes in acceptance of MMR from time to time.