Improving access and quality of primary healthcare through women and adolescents' user committees: A mixed‐methods case study in Kinshasa, Democratic Republic of the Congo
Bibliographic record
Abstract
BACKGROUND: Patient engagement is seen as a fundamental strategy for achieving quality patient-centred care, especially in community-based primary healthcare. Despite growing interest in patient engagement in Sub-Saharan Africa, few patient engagement initiatives have been identified, and those often are limited to lower levels of engagement, in participation in health research or in health system improvement. With the aim of giving a voice to under-represented community groups in healthcare governance, the Access to Health services in Kinshasa (ASSK) project supported the implementation of primary health services user committees in the Democratic Republic of the Congo, designed to enable the representation of two user groups with specific unmet sexual and reproductive health (SRH) needs: women and adolescents. AIMS AND METHODS: Using a mixed-method case study design combining quantitative secondary data (from the national health management information system-DHIS2) and qualitative data from two research World Café (WC1: Women user committees (WUC) n = 55; WC2: Adolescents user committee (AUC) n = 63), this paper looks at the implementation facilitators and barriers, and at the results of this initiative. RESULTS: Women and adolescent members of the user committees highlighted that their participation resulted in increased knowledge of SRH and their related rights, as well as in their 'soft skills' such as communication and leadership. In addition, participants reported greater transparency and accountability on the part of the community primary health centres (e.g. by displaying fees for procedures to counter over-billing). Ultimately, WUC and AUC were associated with improved health practices in the community such as increased use of SRH services (increase of 613% for Makala and 160% for Maluku II), including adolescent family planning (increase of 320% for Makala and 12% for Maluku II) and assisted childbirth for women15-49 years old (increase of 283% for Makala and 23% for Maluku II)). CONCLUSIONS: Patient user committees for specific marginalised or under-represented groups appear to be an effective way of improving the quality of primary health care services. Further research is needed to better understand how to maximise its potential.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".