Proposals for IR projects differ from those used in other types of research primarily because IR originates from a problem identified and prioritized by end-users – so that the research findings can be used within the available health system framework and implemented appropriately for end-users’ immediate benefit. Developing an IR project proposal from an intersectional gender perspective is critical for addressing implementation bottlenecks (Figure 8). (See: Components of an IR proposal)
Integration of the intersectional gender perspective should start at proposal development stage of the IR process,22 which includes conceptualization of the research, problem analysis, research design and plans for data collection, analysis and implementation, and dissemination of research findings.
The rationale of an IR study should be convincing to relevant funding agencies and policy-makers, so they will potentially commit resources to your IR project and make relevant policy decisions or changes informed by the results of the study. In the rationale of an IR proposal, the importance of the research – in relation to existing local and national research agendas or policies – should be clearly described and justify why the study needs to be conducted. State how the ‘voices’ of the vulnerable population will be incorporated and harnessed to draw the necessary recommendations that will enhance their access to the intervention, including at different levels of the health system.40,41 This can be achieved by clearly describing how participants will be selected during stakeholder and community engagement steps to ensure diversity and representation of vulnerable populations in the study context. (See: Related module of the WHO/TDR Intersectional gender analysis toolkit)
The research problem should be of interest and justifiable to all stakeholders (e.g. researchers, policy-makers, decision-makers, funding agencies, care providers and the community affected by the research). In your problem statement, describe the problem, its magnitude, the current health practices, health-seeking behaviours of vulnerable populations and factors preventing them from accessing the intervention. Describe what might be the gender-related challenges and opportunities for the proposed IR solutions. Specifically, using an intersectional gender lens, explore how gender dimensions interact with other social variables (e.g. socioeconomic status, sexuality, age, refugee status, geographic location or religion, among others), to influence implementation of your IR study. (See: Related module of the WHO/TDR Intersectional gender analysis toolkit)
Research questions should be of interest and relevance to all project stakeholders. (See: Introduction to module on ‘Developing an IR proposal’)
Informed by gender frameworks, intersectional gender analysis questions can be developed to guide researchers in the overall direction of the study, including informing research objectives, developing research questions and hypotheses. These questions help researchers move beyond describing the differences between men/boys, women/girls, and people with non-binary identities, to examine and critically interpret how gender inequities manifest within a particular context, how they intersect with and are influenced by other drivers of inequality, and their effect on IR.1
When developing intersectional gender-informed IR questions, consider the different social variables within the inner circle of the intersectionality wheel,15 that interact to shape individual experience under the contextual factors in which the IR project is being conducted. (See: Contextual factors in IR). Key contextual factors – such as physical factors, political environment, economic, social and cultural structures, health systems etc. – should be analysed objectively to ensure that the research questions are formulated and framed considering such factors.
IR questions must be sensitive to the diverse characteristics of the IR project target population (e.g. gender identity, age, social status, (dis)ability, sexual orientation etc.). An intersectional gender perspective does not assume the same experience across population sub-groups (e.g. not all pregnant women in the same geographic area experience similar barriers to access health care). This reflects that decision-making is influenced by different systems and structures of power as well as other factors that influence access to social, economic and political resources. For example, Morgan et al,42 established that at a societal level, pregnant women with disabilities in a Ugandan community were shunned by the men who were responsible for their pregnancy, while at the health facility level, the health workers’ poor attitudes and behaviours towards them were derogatory, which consequently negatively affected their maternal health-seeking behaviours.
This also highlights differences between gender analysis and intersectional gender analysis research questions, as illustrated in Table 5.
To develop gender analysis questions for IR, recognized implementation outcome variables should be used to develop related gender analysis questions. Various implementation outcome variables have been devised that act as indicators of how well the intervention is working.43,44 The variables are acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration and sustainability. (See: Definitions of implementation outcomes)
While constructing intersectional gender analysis questions, it is important to ask: How does this differ between different groups of men, women and non-binary people? How does gender intersect with other social variables (e.g. age, gender identity, education) to create differences between different groups of men, women and non-binary people? Table 6 shows an example of intersectional gender analysis questions informed by a gender framework,16 mapped against feasibility, one of the implementation outcome variables. (See: Further examples of intersectional gender analysis questions)
A literature review provides a foundation of knowledge on a given research topic. (See: Introduction to module on ‘Developing an IR proposal’)
To incorporate an intersectional gender perspective, focus the literature search on exploring how gender intersects with other social variables or axes of inequality in relation to your IR problem.1 Use keywords sensitive to gender and intersectionality. For example, O’Neill et al45 explored the utility of using an acronym PROGRESS (i.e. place of residence, race/ethnicity/culture/language, occupation, gender/sex, religion, education, socioeconomic status, and social capital) while conducting 11 systematic reviews and methodological studies published between 2008 and 2013 to assess effects of interventions on health equity. Box 4 shows examples of keywords for intersectional variables that can be used while conducting a literature review.
Consult multiple sources of data including specific community-based research, published and grey literature. Much of the community-based research might not be published in peer-reviewed journals. Therefore, it will be useful to conduct internet searches for the information posted on the respective websites of community organization47 that are active in the geographic area of your IR project. You can also enrich your literature review by citing prior studies that highlight significant similarities and differences between the different social identities, which in turn can inform the thinking behind the research project design.
Research objectives should be specific, measurable, achievable, realistic and time bound. While developing your research objectives, think about which implementation outcomes are appropriate for your study and how they can be measured. IR study objectives with an intersectional gender lens should be aligned with the corresponding research questions and sufficiently strategic to help reduce implementation bottlenecks, thereby promoting access and intervention coverage among the vulnerable target population. In other words, the objectives should contribute to the elimination or alleviation of the negative experiences by the vulnerable target population. Box 5 shows examples of research studies in which research objectives denote an intersectional gender approach.
Research design is the conceptual blueprint or strategy within which research is conducted.50 Various study designs can be employed in IR projects. The different study designs and factors guiding appropriate study design selection have been described in detail elsewhere in this Toolkit. (See: More information on research design)
In this section of your IR proposal, specify the study design and the justification for its adoption. While deciding on your study design, adopt an intersectional gender lens to explore and reflect upon ‘what’, ‘why’ and ‘how’ questions, to uncover how different social variables intersect to influence the implementation of and access to the intervention under consideration.43 The WHO Gender responsive assessment scale4 is a framework used to help determine the extent to which gender is incorporated into research. The scale includes five types of research:
For conducting IR studies and/or health interventions, the gender continuum framework5,51 is useful to help determine how gender is addressed within intervention design and implementation. The framework classifies interventions into:
Figure 9 helps researchers to assess their planned activities against each approach/level to determine the extent to which their research and/or interventions are currently integrating sex and gender.1 (See: Getting to grips with how to approach intersectional gender analysis for research on infectious diseases of poverty)
While conducting your research design exercise, reflect on the data that needs to be collected and disaggregated according to various intersecting variables to facilitate intersectional gender analysis. To be more precise with the results, focus specifically on social variables that can be disaggregated to create meaningful group-level variables (53). The inclusion of such reliable and valid measures allows researchers to explore the complex factors that shape and influence the experiences of individuals influenced by different gender dimensions.
Study designs used in IR can be interventional (e.g. experimental, quasi-experimental, before and after, cohort studies or randomized controlled trials) or observational (e.g. exploratory, descriptive and comparative) studies. For your IR project, you can use quantitative, qualitative research methods or a combination of the two (i.e. mixed methods). (See: Module on ‘Developing an IR proposal’, See: Research methods and Data management)
Key factors to consider while choosing your research methods include:
IR focuses on identifying the challenges and bottlenecks related to the roll out of health interventions, as well as on developing and testing effective strategies designed to overcome them. If the health intervention is new, you can test the acceptability, adoption, appropriateness, feasibility and sustainability of that intervention. For example, if your IR explores barriers and facilitators of access to the intervention by the intended participants in the community, an intersectional gender approach helps to understand the magnitude as well as the contributing factors that influence barriers to access the intervention. Participatory research methods (PRM) place the most vulnerable populations at the centre of research.40,47 PRMs are collaborative and equitably engage all partners in the research process, for example during problem identification and action planning for change, thereby increasing participants’ likelihood of using the research findings for appropriate actions.54,55 (See: Participatory research methods to transform inequitable gender norms)
Furthermore, engaging vulnerable populations enables researchers to appreciate the gender relations at play and how these intersect with other social variables to influence access to the intervention. If the health intervention is well established, you can test its fidelity, cost and coverage. If your study is to learn about the bottlenecks of the implementation of the intervention, then understanding the implementers perspective (e.g. doctors, nurses, community health workers delivering care or treatment) will be helpful for researchers to see how gender differences influence the implementation. The three commonly used research methods that you can employ in IR with an intersectional gender lens are briefly described in the following section.
The use of an intersectional gender lens in qualitative research methods allows greater understanding of people’s lived experiences, and how practices, policies and programmes are responding to the needs of women/girls, men/boys, and people with non-binary identities. You can consider PRM while designing your qualitative study. Some of the different PRM include participatory mapping (e.g. community maps, transect walks), timelines (e.g. life histories, daily activity) as well as priority ranking, Venn diagramming, matrix scoring and use of problem trees. In recent years, participatory action research (PAR) has been used as a tool to encourage both communities and health system actors to recognize their own problems and create solutions that can promote social change.56
In your IR proposal, you should describe the qualitative data collection methods your study will use, including the process followed to identify the study sample. Qualitative research instruments used for data collection in IR include key informant interviews (KIs), focus group discussions (FGDs), observations, documents (e.g. diaries and historical documents), among others. (See: Qualitative data collection tools, See: When to use various qualitative data collection techniques)
Quantitative methods involve the collection and analysis of objective data, often in numerical form and used to examine relationships between variables. The research process, interventions and data collection tools (e.g. questionnaires, observation check lists, performance-based instruments) are standardized to minimize or control possible bias.53 (See: Research methods and data management)
The majority of IR research questions require answers to both the ‘what’ and the ‘why’ aspects and, as a result, require use of mixed methods that include both quantitative and qualitative approaches. If you use a mixed methods approach, you should explain why your team chose the approach, and how the use of qualitative and quantitative methods will provide information to address the research question and objectives. (See: Guide to decision-making on whether to use mixed research methods, See: Main mixed methods research approaches)
Under this section of your proposal, describe: (i) the individuals in the social category of interest; (ii) how gender dynamics and various gender domains interplay in the implementation and outcome of the intervention; (iii) how other axes of inequality and structures of power such as social background, education, sexism, classism, homophobia, or any relevant combination of these, impact on their experience with the health care system. For example, if your study is to test the uptake of an intervention for any noncommunicable disease in primary health care settings, you may wish to consider the differences between men and women who will use the intervention and whether their religion, education, income status, age etc. – given their specific context – intersect to influence their decision to use the intervention.
People often face unique barriers while accessing interventions due to interpersonal, societal and/or structural power dynamics and discriminatory practices. This is especially common for those who may not be socially or legally respected in certain contexts, for example in the case of gender identities beyond the binary gender categories. It is important to intentionally develop and implement a strategy to identify and meet appropriate respondents, avoid any harms their participation might cause them, and ensure key respondents are not being excluded.
For example, in societies with acute health and social inequities across populations, if you are identifying participants from a database of health facility patients, there is a chance you will lose the most vulnerable community members as they may not be accessing services at health facilities. In areas of east Ethiopia, for example, where gender norms dictate son preference, more than 50% of households had increased odds of preferential care-seeking for boys, but decreased odds for girls, compared with communities in which fewer than 50% of households were Muslim.57
You should be mindful not to focus the recruitment entirely from traditionally recognized institutions such as health care facilities and training institutions, and broaden your strategy to other institutions, civil society organizations and human rights networks that can contribute to the recruitment phase of your research project. Other sources to consider for recruitment include advocacy organizations, religious centres, empowerment groups, community centres, unions/fraternities, and web-based locations such as social media, chatrooms, blogs and support groups.58,59
In case of hard-to-reach populations, you can consider using venue-based sampling or time-location sampling (TLS). The TLS strategy assists researchers to intercept hard-to-reach populations in places and times where they might gather.60,61 For example, it can be used for adolescents who may come together to access services provided to them in specific social venues at certain times of the day. Community gatekeepers exist who can be excellent sources to help identify participants. However, selection bias may occur as these gatekeepers have the potential to rule out key participants who might have a language barrier, who are hesitant to speak or for those who might have to seek permission from family members to participate in the study. To ensure that there is no selection bias, it is better to approach different community gatekeepers, preferably of different gender identities and social locations, so that a heterogenous group of people is included in your study.
Under this section of the proposal, describe the steps of your sampling process. The main steps are (62):
In general, sampling techniques can be divided into two types:
In general, probability sampling techniques are typically used in quantitative research methods and non-probability sampling techniques in qualitative research methods. Overall, ensure that the sample is as heterogeneous as possible to allow diversity within the study population. This facilitates representation of those who would have been overlooked.58 Before sampling, it is important to define the inclusion and exclusion criteria for your study. (See: Further information on sampling)
As quantitative studies require a representative sample in relation to population characteristics, a probability sampling is preferable. This enables every individual in the population to have a certain chance of being included in the sample. While planning your sample size, consider how data will be disaggregated in your study. For example, if your study explores barriers to access a health intervention by adolescents residing in a specific geographic location, you will want your sample size to be representative of adolescent boys and girls. In order to incorporate an intersectional gender perspective, it will be helpful to also consider social variables such as education status, religion, marital status etc. as relevant to your study while considering your sample size, so that it is possible to collect disaggregated data for analysis.
In qualitative research, the use of purposive, quota and snowball sampling strategies from an intersectional gender perspective, strengthens the study design and promotes diversity and inclusivity of participants.60,63
With qualitative research, the sample should be designed to allow for in-depth understanding of the role of gender and its intersection with other social variables. Consider the similarities and differences within the study population. The sampling strategy will depend on the objective of the study and the type of analysis (i.e. inter-categorical or intra-categorical) you plan to do.
For inter-categorical analysis, you can divide your sample into different groups according to the relevant social variable that you are studying. For example, to do an intersectional gender analysis of how gender intersects with economic status between different groups of people while seeking health care, your sample will have to be diverse enough that data can be disaggregated into poor men vs poor women vs rich men vs rich women. The sample needs to be as representative as possible with respect to a community or population of interest, while being heterogeneous enough to allow for inductive explorations (e.g. interrogating how various categories can intersect to differentially shape experience).64 An intra-categorical analysis focuses on one specific group at the intersection of multiple social variables to explain within-group differences and larger social structures influencing their lives. For example, if your IR is exploring barriers and facilitators for adolescent girls to seek reproductive health services, then your sample will remain homogenous as you are identifying only adolescent girls. However, while conducting intersectional gender analysis, you have to be mindful that different social variables such as education, religion, etc. of an adolescent girl will intersect to influence her experience of seeking reproductive health services. Thus, within your sample of adolescent girls, you should be able to analyse the differences in their experience arising because of their different social variables and other structural or contextual factors. (See: Data disaggregation as an entry point for further understanding)
As a researcher, you should be cognizant of how power relations, biases and other key factors can influence the quality and validity of the data collected (Table 7).
To increase participation during data collection, outline the measures you will use to give every participant the same opportunity to be involved. Also describe how the research team will ensure confidentiality throughout the entire research process. Privacy, safety and confidentiality should be ensured during data collection, and the research team should be sensitive to existing gender dynamics. For example, in certain contexts or circumstances, women may feel uncomfortable if the data collector/researcher is not a woman. Similarly, the institutional hierarchy may influence junior officers responses during focus groups in the presence of their supervisor or senior manager. At the household level or school setting, adolescent boys or girls might be fearful to participate openly in the presence of a parent, guardian or teacher.
Be aware of the gender dimensions (e.g. gender roles, norms, and relations) that influence the division of labour at the household and community levels. For example, in some communities, gender norms dictate women do unpaid work within the household while men are expected to work outside home to earn a living. Thus, it may be difficult to collect data during certain hours of the day, since both women and men may not be available during the day or season.
Therefore, it is pertinent to be sensitive to gender norms, roles and relations in a given community, to ensure availability of target respondents and confidentiality of responses during the data collection process. Research proposals should describe the process of participant identification, the time periods, and the convenient places for data collection to ensure comprehensive information including from those who tend to be less centrally engaged in the participatory process (See: Key gender considerations within the data collection process).
During the planning phase, it is important to establish baseline indicators to contribute to monitor and measure the progress of your IR project. These should be developed in collaboration with the community and the study population. The intersectional gender analysis questions already considered/developed can be used to inform these indicators. Gender-sensitive indicators can be sex-specific, sex-disaggregated and/or indicators for gender equality. In general terms, indicators should: (i) guide collection of data that can be disaggregated by the relevant social variables; (ii) measure and monitor the achievements of expected results; (iii) measure any gaps in the experiences of the study participants; (iv) avoid large group categorizations that may miss intra-group differences; and (v) be gender sensitive (i.e. measures gender equality directly or is a proxy for gender equality).
Within the data collection tools and indicators, consider gender-related variables/proxies in alignment with the gender domains that are integral parts of your IR study. Table 8 shows examples of gender proxies/variables that support analysis of gender power relations domains against relevant implementation health outcomes.
To develop gender equality indicators explore the role of gender power relations specific to your IR project as included in your gender framework. (See: Developing gender-sensitive indicators)
While developing indicators, consider the relevant IR outcomes that you will measure. For example, if your study is exploring how decision-making influences acceptability of a given health intervention (i.e. IR outcome) for married women, your intersectional gender equality indicator could be:
Proportion (%) of married women aged 15–49 who usually decide to accept the health intervention either by themselves or jointly with their husbands, disaggregated by income, age, education, etc.
Table 9 shows examples of differences between gender-sensitive indicators and intersectional indicators.
It is important to clearly outline the plan for data analysis in your IR proposal. Both the techniques and models for data analysis should be in accordance with the study objectives, research methods used and the types of anticipated IR outcome variables. The data analysis plan should have the target audience in mind with a focus on simplicity and interpretability. Clearly explain the analyses you intend to conduct on the data. Indicate the appropriate software you may use in the data analysis. (See: Further guidance on data analysis)
To analyse data effectively using an intersectional gender lens, the IR team should have taken preparatory steps from the initial stages of the study design. This includes disaggregation of data or sampling frameworks by sex and other social variables, the use of gender frameworks and the incorporation of intersectional gender analysis questions into data collection tools. (See: Analysing research data using an intersectional gender lens)
It is useful to develop an intersectional gender analysis matrix relevant to your study at the beginning of the proposal development process. Because it is difficult to ask about gender power relations directly, gender frameworks are used to break down the ways in which they manifest and then develop proxies to indirectly analyse gender relations against relevant health or other outcomes. An intersectional gender analysis matrix can be used to help you think about which domains might be most relevant for your study. Researchers should begin by filling in the matrix by identifying how the different gender relations domains may affect areas of interest relevant to your study, and which social variables are likely to intersect with gender to influence a person’s marginalization or vulnerability regarding these domains.
Table 10 illustrates an example of using the intersectional gender analysis matrix while conducting research in infectious diseases. This helps researchers to identify how gender relations domains affect the infectious diseases domains, and helps to identify which social variables can potentially intersect with gender to influence an individual’s vulnerability. It is important to develop an intersectional gender analysis matrix specifically for the relevant gender domain, study domain and social variable relevant to your research. For example, if you are planning to conduct IR on access to bed nets by adolescent boys and girls in a dengue endemic area, to ascertain their ability to prevent exposure to mosquito bites, you can identify the contextually-relevant gender norms, relations and values and also consider which specific social variables intersect with the boys/girls access to bed nets. If gender norms allow only adolescent boys to wear shorts (i.e. unprotected clothing), this will decrease their ability to prevent mosquito bite exposure as compared to girls. In this scenario, the possible social variables that can be considered to influence risk of exposure may include age, sex, race/ethnicity, education status and socioeconomic status.
Data can be analysed in two different ways:
a) Intra-categorical focusing on one social group only and analysing experiences of that one group (e.g. focusing only on adolescent boys and analysing how their age, sex, race/ethnicity, education status and socioeconomic status intersect to influence their access to bed nets, thus affecting their ability to prevent exposure).
b) Inter-categorical (e.g. analysing data for differences between both adolescent boys and girls and across social variables such as age, sex, race/ethnicity, education status and socioeconomic status). For example, you can identify and compare differences and experiences in terms of vulnerability to disease exposure across social groups such as poor uneducated boys and poor uneducated girls.
Before analysing quantitative research data using an intersectional gender lens, your data should be disaggregated by variables relevant to your IR study. Depending on your research design, analysis can be intra-categorical or inter-categorical. In both approaches, the analysis focuses on the intersection of selected social variables to understand how these variables interact to create different experiences of marginalization and discrimination, which in turn shape health outcomes related to your IR study.
It is also possible to conduct a gender analysis on secondary quantitative data, such as demographic health surveys, population-based surveys or own quantitative data sets. Generally, the secondary data sets help further sex-specific (males or females) and sex-disaggregated (males and females) analysis. For example, if you are studying the prevalence of malaria in a population residing in an endemic area, you can conduct a sex-specific analysis for males and females separately. To conduct a sex-disaggregated analysis, the differences in prevalence between males and females diagnosed with malaria are considered. However, to conduct an intersectional sex-specific analysis, you must disaggregate this data by the relevant variable chosen for your study (e.g. age, education, ethnicity etc.). Intersectional sex-disaggregated analysis explores the prevalence of malaria between and among groups of males and females, against the different variables chosen as relevant for your IR study.
Generally, data cannot be disaggregated by gender in the same way it can be disaggregated by sex. Therefore, relevant gender relations domains need to be included within data collection tools and interrogated separately; these are sometimes referred to as gender variables and are used as proxies to understand gender relations.1 Refer to Tables 8 and 9 to identify gender variables/proxies and intersectional gender indicators, respectively.
Unlike traditional quantitative methods, intersectionality-informed analysis uses an additive approach, using an initial ‘baseline’ upon which further analyses are applied using multiplicativity (e.g. regression coefficient) to account for effects of intersecting categories on health or social outcomes.
Intersectional gender analysis begins during data collection, when researchers are gathering and reflecting iteratively on the data and practicing reflexivity throughout the coding process as well as subsequent interpretation and reporting. Regardless of the level of analysis or approach, it is important to note that expectations and potential biases of the researcher must be open, particularly those resulting from the interaction between the data and the researchers’ backgrounds. Caution should be taken to avoid reproducing inequality within the data coding and analytic processes.65 A multi-stage analysis is needed to enable moving from additive towards interactive analysis. When analysing data, you will therefore need to go beneath the surface of what is being stated/said to understand how gender intersects with other social variables to influence different experiences, relating this to the larger social, political and cultural context. Data analysis often occurs on one level, the semantic level, which involves analysing data at face value, only considering what participants have articulated or written. However, to conduct an intersectional gender analysis, researchers must go deeper to understand and identify assumptions, beliefs, thought patterns and conceptualizations that characterize semantic content. This is particularly true in instances where a person’s identity may be so normalized/ingrained, they may not see how their experiences are shaped by systems or structures of privilege and/or oppression resulting from that identity, therefore, it the researcher’s responsibility to make these connections. This interpretative analysis helps achieve a more comprehensive analysis.22,66
Gender frameworks can be used to develop coding frameworks that facilitate the analysis of qualitative data. In terms of analysis, the type of coding methodology is often based on the types of framing used. As such, inductive analysis should be used, when possible, as it allows for codes to be derived from existing data. To facilitate intersectional gender analysis within qualitative research, a multi-stage analysis is needed. There are three main levels of coding:
Open coding, which involves analysis of data that codes a passage using multiple and overlapping codes (e.g. access to resources, gender norms, gender roles, decision-making, age, etc.).
Axial coding, which focuses on inductively refining each separate code into more distinct codes (e.g. a code for the intersections of gender roles with age, one for intersection of gender roles and poverty, etc.). These codes are often developed following identification of relationships and patterns that emerge during the open coding stage. Grouping open codes into different themes that help explain what is going on can facilitate identification of axial codes.
Selective coding is used to further refine codes to reflect a specific aspect of intersectional experience (e.g. how married women’s experience of assigned domestic responsibilities influences her access to a health intervention). These codes often link the intersections of different social variables to experiences of advantage or disadvantage in relation to the implementation outcome of a given IR study. (See: Conducting intersectional sex-disaggregated and/or sex-specific analyses)
Box 6 illustrates an example of how gender domains can influence men’s health-seeking behaviours.
As in other forms of research, ethical considerations are of vital importance to IR with an intersectional gender perspective. Respecting the dignity of all research participants and avoiding causing any physical, emotional or psychological harm to study participants are essential throughout the entire research process. It is important to take extra caution to minimize the risks that may be associated with working with vulnerable populations. It is also essential to be cognizant of sensitive issues in relation to the local context, for example, and to use the language of the participant community and respect how the community identifies itself. This communicates respect for their right to self-determination and respects their lives.70 Participatory approaches may be particularly useful, as they can allow individuals who represent the population of interest to work with researchers to ensure linguistic and cultural appropriateness of written or verbal consent documents, for example. Research should be approached with ‘cultural humility’ in communities where any lingering historical mistrust of researchers may exist, as in many marginalized communities for example, due to past unethical research practice.71 (See: Ethical challenges in IR, See: Research design)
Communicating research plans and findings are among the good research practices in IR. Communicating research findings makes you accountable to participants and to the research process itself. Disseminating research findings – especially to the research participants – not only provides them with data, but also sensitizes them to related issues, and enables them to utilize the findings to improve their health-seeking practices (28). Your proposal should include a section on your dissemination plans, including where and to what audiences you intend to disseminate your research findings. As much as possible, you should aim to communicate the results and findings of your research to all the stakeholders engaged in the research effort, using the most appropriate and relevant channels.
The dissemination plan should include:
During proposal development, it is also important to consider how a gender lens will be used in reporting of study findings. The first step is to ensure development of gender-sensitive reports considering how men, women and people with non- binary identities will be differently affected by the results. While writing a gender-sensitive report, be cautious that potentially harmful gendered stereotypes are not replicated. When conducting gender analysis, common pitfalls that may bias research include:1,72
Some key questions to be considered while generating gender-sensitive reports are: