Gender-responsive monitoring and evaluation for health systems
Bibliographic record
Abstract
Gender-responsive monitoring and evaluation (M&E) for health and health systems interventions and programs is vital to improve health, health systems, and gender equality outcomes. It can be used to identify and address gender disparities in program participation, outcomes and benefits, as well as ensure that programs are designed and implemented in a way that is inclusive and accessible for all. While gender-responsive M&E is most effective when interventions and programs intentionally integrate a gender lens, it is relevant for all health systems programs and interventions. Within the literature, gender-responsive M&E is defined in different and diverse ways, making it difficult to operationalize. This is compounded by the complexity and multi-faceted nature of gender. Within this methodological musing, we present our evolving approach to gender-responsive M&E which we are operationalizing within the Monitoring for Gender and Equity project. We define gender-responsive M&E as intentionally integrating the needs, rights, preferences of, and power relations among, women and girls, men and boys, and gender minority individuals, as well as across social, political, economic, and health systems in M&E processes. This is done through the integration of different types of gender data and indicators, including: sex- or gender-specific, sex- or gender-disaggregated, sex- or gender-specific/disaggregated which incorporate needs, rights and preferences, and gender power relations and systems indicators. Examples of each of these are included within the paper. Active approaches can also enhance the gender-responsiveness of any M&E activities, including incorporating an intersectional lens and tailoring the types of data and indicators included and processes used to the specific context. Incorporating gender into the programmatic cycle, including M&E, can lead to more fit-for-purpose, effective and equitable programs and interventions. The framework presented in this paper provides an outline of how to do this, enabling the uptake of gender-responsive M&E.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".