Adapting the capacities and vulnerabilities approach: a gender analysis tool
Bibliographic record
Abstract
Gender analysis methodology is increasingly being considered as essential to health research because 'women's social, economic and political status undermine their ability to protect and promote their own physical, emotional and mental health, including their effective use of health information and services' {World Health Organization [Gender Analysis in Health: a review of selected tools. 2003; www.who.int/gender/documents/en/Gender. ANALYSIS: pdf (20 February 2008, date last accessed)]}. By examining gendered roles, responsibilities and norms through the lens of gender analysis, we can develop an in-depth understanding of social power differentials, and be better able to address gender inequalities and inequities within institutions and between men and women. When conducting gender analysis, tools and frameworks may help to aid community engagement and to provide a framework to ensure that relevant gendered nuances are assessed. The capacities and vulnerabilities approach (CVA) is one such gender analysis framework that critically considers gender and its associated roles, responsibilities and power dynamics in a particular community and seeks to meet a social need of that particular community. Although the original intent of the CVA was to guide humanitarian intervention and disaster preparedness, we adapted this framework to a different context, which focuses on identifying and addressing emerging problems and social issues in a particular community or area that affect their specific needs, such as an infectious disease outbreak or difficulty accessing health information and resources. We provide an example of our CVA adaptation, which served to facilitate a better understanding of how health-related disparities affect Maasai women in a remote, resource-poor setting in Northern Tanzania.
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 | 0.001 | 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".