In this step, you will consider how to monitor and evaluate your policy by gender.
How will you test whether, and in what ways, the situation for groups of women and men has improved?
Make gender an explicit part of your intended monitoring and evaluation of effectiveness, based on the analysis you’ve done in previous steps. If you are not explicit about your intention to monitor and evaluate by gender in policy documents, any impacts by gender are unlikely to be evaluated and will consequently be ignored.
How are groups of women and men impacted compared to before the policy intervention? If you are delivering a service, which groups of women and men are accessing the resources or opportunities? Are these groups the ones you expected, or are they different groups of women and men? For instance, motherhood is a common ‘hidden’ factor that can affect service uptake. Access to transport and working only around business hours are related factors.
Establish baseline indicators to measure the effectiveness of your intervention by gender. Are the indicators conducive to assessing the impact on various sub-groups of the target population? The question you can ask yourself is: In 12 months’ time, if I’m asked how well diverse groups of women have been impacted or assisted, how will I know?
In order to capture the different circumstances of groups of women and men, you may need different measures of success and progress.
How will you ensure that monitoring systems collect data by gender and other relevant identity factors such as ethnicity?
You’ll need to ensure that your monitoring intentions are carried out. There are often data gaps by gender (particularly for gender-diverse people) and by other identity factors. Disaggregating data to identify impacts on particular groups of women (e.g. wāhine Māori), is often difficult.
In the past, government forms (from the national census to medical or education enrolment forms) have provided only two choices when it comes to gender: male or female. People who identify as gender diverse are excluded by this approach, and they are also made invisible for statistical purposes. As a result, services don’t fit all individuals and information reinforces a binary view. Inaccurate information is used for planning services or allocating funding. Statistics New Zealand has now developed standards for collecting gender information and is providing non-binary choices in some surveys.
Check that your data collection guidelines, forms and processes ensure that data collected can be disaggregated by gender as well as by other relevant factors and personal characteristics (e.g. ethnicity or parental status). Are there gaps in the data that’s collected? If so, what changes are needed to the systems or what further data collection methods would best measure outcomes?
How will the policy intervention be changed if it is not delivering as expected?
If you find differences in how groups of women are impacted, what are the underlying reasons for those differences? Asking further questions about needs, experiences and expectations may be useful. If delivering a service, what are the barriers to access for groups? How can those barriers be addressed?
What will be the feedback loop from any monitoring to further policy change? Do you have an effective feedback loop from complaints to monitoring to policy so that issues are raised?
Example: Evaluating the Trans-Affirmative Healthcare Pilot Clinic at Mauri Ora Student Health
The Trans-Affirmative Healthcare Pilot Clinic (the clinic) at Mauri Ora Student Health is an innovative, community-based approach to providing gender-affirming hormone therapy to gender-diverse students at Victoria University.
An estimated 1.2 percent of all young New Zealanders identify as transgender or gender-diverse; their gender differs from their sex assigned at birth. A further 2.5 percent report questioning their gender identity. Many gender-diverse people experience gender dysphoria, the distress associated with dissonance between a person’s gender and their body. Gender-affirming hormone therapy (GAHT) is effective in reducing dysphoria and improving mental health and wellbeing. It has traditionally been provided in secondary care settings, but this has created barriers to access and lengthy wait times for care, exacerbating distress and resulting in high health costs.
A recent evaluation of the clinic strongly indicates that this model is an effective way of providing care that benefits both service users and providers. This small, in-depth evaluation of the pilot shows that the clinic:
- ensures holistic, affirmative, timely and ongoing support for service users
- upskills primary care providers, leading to enhanced care for gender-diverse people
- can be readily adapted into other primary care settings.
Example: Rebuilding Christchurch after the earthquakes
Members of the Canterbury Women in Construction Working Group brought gender into monitoring and evaluation inside their own organisations. For example, the Stronger Christchurch Infrastructure Rebuild Team (SCIRT) set an overall goal of 13 percent for women in its operational roles by 2016. Between 2013 and 2014 the number of women in trades at SCIRT overall increased by 50 percent.
Completing Step 6
As you consider your responses to the questions above, you may want to capture your thinking in the downloadable worksheet below.