Examining the gender imbalance in the National Community Health Assistant Programme in Liberia: a qualitative analysis of policy and Programme implementation
Bibliographic record
Abstract
The Revised National Community Health Services Policy (2016-2021) (RNCHSP) and its programme implementation, the Liberian National Community Health Assistant Programme (NCHAP), exhibit a critical gender imbalance among the Community Health Assistants (CHAs) as only 17% are women. This study was designed to assess the gender responsiveness of the RNCHSP and its programme implementation in five counties across Liberia to identify opportunities to improve gender equity in the programme. Using qualitative methods, 16 semi-structured interviews were conducted with policymakers and 32 with CHAs, other members of the community health workforce and community members. The study found that despite the Government of Liberia's intention to prioritize women in the recruitment and selection of CHAs, the planning and implementation of the RNCHSP were not gender responsive. While the role of community structures, such as Community Health Committees, in the nomination and selection of CHAs is central to community ownership of the programme, unfavourable gender norms influenced women's nomination to become CHAs. Cultural, social and religious perceptions and practices of gender created inequitable expectations that negatively influenced the recruitment of women CHAs. In particular, the education requirement for CHAs posed a significant barrier to women's nomination and selection as CHAs, due to disparities in access to education for girls in Liberia. The inequitable gender balance of CHAs has impacted the accessibility, acceptability and affordability of community healthcare services, particularly among women. Strengthening the gender responsiveness within the RNCHSP and its programme implementation is key to fostering gender equity among the health workforce and strengthening a key pillar of the health system. Employing gender responsive policies and programme will likely increase the effectiveness of community healthcare services.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".