Profiles of Community Care Sites and provider Community Health Workers: A Case study at Gombe Matadi, Kenge and Kisantu Rural Health Zones in the Democratic Republic of Congo
Bibliographic record
Abstract
BACKGROUND: In sub-Saharan Africa, the DRC is ones of countries with high infant and child mortality. To solve this problem, the Ministry of Public Health has set up a new program: ICCM at the level of Community Care Sites managed by non-health professionals who are provider CHWs to serve Health Areas with difficult access. Since its implementation, there have not yet been many studies that concretely describe the functionality of CCS. To contribute to the extension of CCC in other Rural Health Zones, we conducted a study in 3 RHZ by choosing the Realistic Evaluation Approach to analyze the functionality of CCS in the DRC. METHODS: This is a cross-sectional study which is essentially a case study. Data were collected at the CCS level in these 3 RHZ chosen after multistage random sampling by mixed method (qualitative, quantitative). We used the following techniques: documentary review, direct observation at the CCS level, an in-depth interview with registered nurses and members of the health Zone management team and a survey using quantitative questionnaire with provider CHWs. The conceptual model that we developed is inspired by realist evaluation with an emphasis on Mechanisms. The relationship between the dependent variable (CCS functionality) and the independent variables was proven using Odds Ratio and Multiple Logistic Regression. RESULTS: CCS functionality and provider CHW profile were measured through the standards enacted for CCS implantation. At the significance level (p=0.05) has been in place for 3 years or more increases by 7 times chance that it is functional (OR = 6.7; p = 0.000). Has household been located less than 5 km from CCS increases by 4 times chance that the CCS is functional (OR = 7.04; p = 0.034). Has provider CHW is regularly trained and supervised increases by 10 times chance that the CCS is functional (OR = 10.01; p = 0.031). If provider CHW participate in Community cell animation meetings (OR = 4.34; p = 0.009) and CCS Management is done by CCS Management team increase by 4 times chance that this CCS is functional (OR = 3.6; p = 0.002). Finally, if there is an initiative for funding CCS increases by 8 times CCS functionality (OR = 8.69; p = 0.009). CONCLUSION: CCS are functional in the 3 RHZs. Their organization, establishment and operation are directly linked to the provision of services to populations living mainly in health areas with difficult access, the availability of inputs of three ICCM-C diseases management, namely uncomplicated malaria, diarrhoea and pneumonia as well as building provider community health workers capacity to ensure their management. This study shows an effective contribution of provider CHWs on CCS functionality in health areas with difficult access.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".