Engendering ethics: recognition and inclusion of intersectional identities in queer communities when conducting population survey research

Figures & data

Figure 1. Interrelationship between sex, sexual orientation, and gender.

Table 1. Recommendations for ethical considerations in population research.

Research dimension	Best practice in decision-making	Guiding Principles and Literature
Research Design	Critique whether information related to sex, gender, and or sexual orientation is relevant to the study. Noting the personal nature of these questions, and the distress that they may cause, these questions should not be asked within a survey ‘just’ for demographic data but should instead have a viable purpose for the analysis of the data.	Non-Maleficence, Beneficence (Beckford-Jarrett et al. 2020).
	Ground research in lived experiences and community involvement to ensure respect for participants, informed decision-making (and consent), and promotion of benefits.	Autonomy and Beneficence (Adams et al. 2017; Ellsberg and Heise 2002; Newman and Kaloupek 2009).
	Due to the possibility of confusion, ensure that all gender, sex, or sexual orientation terms are defined (preferably during the lived experiences and community involvement) for the participants and easily accessible within the survey to ensure researchers and participants are on equal footing and understand what they are answering.	Beneficence and Justice (Bale and Epperson 2017)
Research Instruments	Consider the impacts of gender binarism and heteronormativity on individuals’ lived experiences (Adams et al. 2017; Henrickson et al. 2020), particularly the need for privacy, confidentiality, and how and when the information is obtained.	Beneficence (Beckford-Jarrett et al. 2020).
	All for self-description of personal identities in short response form in order to allow for language accuracy and participant empowerment.	Autonomy (Cass 1979, 1984, 1996).
	Appropriate use of taxonomy and language to ensure research promotes inclusivity and removal of stigma.	Non-Maleficence and Justice (Adams et al. 2017; Henrickson et al. 2020; Staples et al. 2018).
Analysis and Interpretation	Ensure the research team adequately understand their biases and assumptions to ensure they minimally impact the research and participants (i.e. researcher reflexivity, relational accountability).	Non-Maleficence and Justice (Henrickson et al. 2020)
	Ensure analysis and interpretation of data are not extrapolated or generalized inappropriately.	Non-Maleficence (Campbell, Goodman-Williams, and Javorka 2019; Paquin, Tao, and Budge 2019)
Dissemination of Findings	Provide the community with the outcome of the research and findings are not manipulated by the researcher’s own agenda.	Beneficence, Non- Maleficence and Justice (Adams et al. 2017; Rogers and Kelly 2011)

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