You are a senior MEAL specialist tasked with conducting a comprehensive gender analysis for [YOUR PROGRAM NAME, e.g., 'Community Health Initiative'].
Your analysis should be based on the following data sources: [YOUR DATA SOURCE, e.g., baseline survey] and [NEW DATA SOURCE, e.g., stakeholder interviews]. If available, incorporate at least 3 years of data from [YOUR DATA SOURCE, e.g., M&E dashboard].
Disaggregate your findings by [DISAGGREGATION VARIABLES, e.g., gender, age group, disability status] and consider testing alternative variables such as [ALTERNATIVE DISAGGREGATION VARIABLES, e.g., socioeconomic status].
Evaluate both quantitative outcomes, focusing on [KEY METRICS, e.g., service uptake, income levels, employment rates], and qualitative insights from the attached or pasted document.
Identify gender-disaggregated trends in [KEY OUTCOME, e.g., service uptake] and compare impacts between [DISAGGREGATION VARIABLES, e.g., gender, age group]. Specifically, identify systemic barriers and inclusion gaps in [PROGRAM COMPONENT, e.g., training sessions, vocational training].
Compare your results to [COMPARISON FRAMEWORK, e.g., national gender equality targets] and [COMPARISON FRAMEWORK, e.g., regional benchmarks].
Your output must include:
- An executive summary highlighting key gender differences in outcomes and participation rates.
- A revised disparity analysis, including cross-tabulated data tables and graphs showing disparity trends.
- A root cause analysis of exclusion and systemic barriers to inclusion.
- SMART recommendations for program adaptation and addressing identified gaps, prioritizing actionable recommendations for marginalized groups.
- An explanation of methodological improvements and any flagged assumptions needing verification.
- A list of data limitations affecting the analysis.
Ensure your analysis checks for fully disaggregated data and looks for unexpected patterns in participation or outcomes.
If the material is incomplete: Work only from the data sources actually provided, such as the survey, interview, or dashboard data described. If a disaggregation variable, data source, or benchmark needed for a comparison is missing, say so in that part of the analysis and continue with what the data supports. Do not invent disparity figures, quotes, or benchmark comparisons that are not in the material given.
Anything still in [brackets] is not known yet: ask me, or mark it clearly in your answer. Where the material is silent, write "Not in the material" rather than guessing.
Data to analyze: Attached, or pasted below. Base everything above on it and do not invent details that are not in it.