The Practice of Gender and Protection Mainstreaming in Health Response in Humanitarian Crisis - A Case Study from the Refugee Camps in Cox’s Bazar, Bangladesh
Bibliographic record
Abstract
Abstract Background The health system is required to be safe, equitable, and accessible to all ages, gender, and vulnerable groups, including older persons and persons with disability, and address their specific needs and concerns. However, limited evidence is available on the effectiveness and practicality of gender and protection mainstreaming interventions in health response in humanitarian crises. Objective The overall objective of the research was to explore practices, gaps, and challenges and generate recommendations regarding gender and protection mainstreaming in health response to the Rohingya refugee crisis in Cox’s Bazar, Bangladesh. Methodology The research employed a qualitative case study design to explore the practice of gender and protection mainstreaming in health response in Cox’s Bazar. Data collection methods include an extensive literature review and in-depth interviews with professionals. Result The study found a range of good practices on gender and protection mainstreaming in health response, e.g. placement of a gender action plan, monitoring system for gender and disability inclusion, emergency preparedness and response system, availability of sex-segregated toilets and waiting spaces, availability of gender-based violence service and engagement of female community health workers. The study also revealed some best practices which have the potential to scale up, e,g. psycho-social spaces at health facilities for children, palliative care for terminally ill patients, integrated medical and protection services, and facilitation of community health facility support groups. Critical gaps were found in the areas of women’s leadership, coordination, capacity building, targeted interventions for vulnerable groups, infrastructural adaptation and consultation with the community on their concerns. Conclusion We urge policymakers, sector coordinators, health program management, healthcare workers, and global stakeholders to address the gaps and challenges, learn and scale up the best practices, and take action to implement the study’s recommendations to maximise gender and protection mainstreaming in health response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".