Essential metrics for assessing sex & gender integration in health research proposals involving human participants

Abstract

Integrating sex and gender in health research is essential to produce the best possible evidence to inform health care. Comprehensive integration of sex and gender requires considering these variables from the very beginning of the research process, starting at the proposal stage. To promote excellence in sex and gender integration, we have developed a set of metrics to assess the quality of sex and gender integration in research proposals. These metrics are designed to assist both researchers in developing proposals and reviewers in making funding decisions. We developed this tool through an iterative three-stage method involving 1) review of existing sex and gender integration resources and initial metrics design, 2) expert review and feedback via anonymous online survey (Likert scale and open-ended questions), and 3) analysis of feedback data and collective revision of the metrics. We received feedback on the initial metrics draft from 20 reviewers with expertise in conducting sex- and/or gender-based health research. The majority of reviewers responded positively to questions regarding the utility, clarity and completeness of the metrics, and all reviewers provided responses to open-ended questions about suggestions for improvements. Coding and analysis of responses identified three domains for improvement: clarifying terminology, refining content, and broadening applicability. Based on this analysis we revised the metrics into the Essential Metrics for Assessing Sex and Gender Integration in Health Research Proposals Involving Human Participants, which outlines criteria for excellence within each proposal component and provides illustrative examples to support implementation. By enhancing the quality of sex and gender integration in proposals, the metrics will help to foster comprehensive, meaningful integration of sex and gender throughout each stage of the research process, resulting in better quality evidence to inform health care for all.

Fig 1. Reviewer responses to Likert scale questions on the metrics.

Fig 1 shows the number of respondents that indicated strongly disagree, disagree, neither agree nor disagree, agree, and strongly disagree in response to each of the 5 survey questions.