4. Discussion
MRS
The NRMN SETH randomized study aimed to test the effectiveness of adding DN to structured coaching in enhancing the research capabilities of ESIs to support career advancement to independent investigators.At the beginning of this project we added NIH-type MRS to enhance the scientific rigor of ESIs’ applications for funding. In this manuscript, we evaluated the association between going through an MRS and the likelihood of (1) submitting an application for extramural funding and (2) being awarded funding. All submitted applications, as we speak, were included in these analyses. However, to evaluate the impact of MRS on the likelihood of being awarded funding, i.e., to properly determine the association between going through an MRS and getting funded, we needed to allow for the ”lag” time between submission and decision and consider several other predictive/mediating factors like the time-to-submission, whether for an initial application or a resubmission, the ESI’s background discipline, pre-existing self-established readiness (measured for example by number of lead-authored publications in high tier journals), existence of a mentor and existence of within-institution supporting development/incentivizing programs/resources. These factors will be considered in future analyses when we report the final findings of the NRMN SETH randomized study.Our results showed a significant association (that can also be potentially construed as an impact) between going through an MRS and the likelihood of submitting an application for extramural funding. ESIs who went through an MRS (versus those who did not) were at least 4.8 times as likely to submit for funding (95% confidence interval = (2.8, 9.4); Our analyses also revealed that earlier cohorts i.e., 1–2 versus later cohorts, i.e., 3–4, were more likely to submit an application for funding. Cohorts 1–2 were 3.8 times as likely to submit an application for funding (95% confidence interval = (2.1, 6.9); Multivariate analyses also indicated that R-series and other mechanisms of funding seekers (versus K-series mechanism) were, respectively, 2.3 and 5.8 times as likely to submit an application for funding (95% confidence intervals and Our study also determined the most prevalent deficient issues deemed by MRS experts consequent to reviewing ESIs’ applications prior to their submission dates. Lack of or insufficient components of statistical analyses plans, like missing/generic sample size and statistical power justifications, and lack of (a) clarity identifying outcome measures and (b) covariates/confounders/predictors of outcome, were most prevalent, comprising 75% of the issues. Second to the statistical consideration issues was lack of conceptual model/framework which comprised 28.3% of the deficiencies. Other issues that were also deemed deficient were overlapping specific aims, insufficient justification/evidence of significance, and insufficient justification/evidence of innovation.