Household coverage of Swaziland's national community health worker programme: a cross‐sectional population‐based study
Bibliographic record
Abstract
OBJECTIVES: To ascertain household coverage achieved by Swaziland's national community health worker (CHW) programme and differences in household coverage across clients' sociodemographic characteristics. METHODS: Household survey from June to September 2015 in two of Swaziland's four administrative regions using two-stage cluster random sampling. Interviewers administered a questionnaire to all household members in 1542 households across 85 census enumeration areas. RESULTS: While the CHW programme aims to cover all households in the country, only 44.5% (95% confidence interval: 38.0% to 51.1%) reported that they had ever been visited by a CHW. In both uni- and multivariable regressions, coverage was negatively associated with household wealth (OR for most vs. least wealthy quartile: 0.30 [0.16 to 0.58], P < 0.001) and education (OR for >secondary schooling vs. no schooling: 0.65 [0.47 to 0.90], P = 0.009), and positively associated with residing in a rural area (OR: 2.95 [1.77 to 4.91], P < 0.001). Coverage varied widely between census enumeration areas. CONCLUSIONS: Swaziland's national CHW programme is falling far short of its coverage goal. To improve coverage, the programme would likely need to recruit additional CHWs and/or assign more households to each CHW. Alternatively, changing the programme's ambitious coverage goal to visiting only certain types of households would likely reduce existing arbitrary differences in coverage between households and communities. This study highlights the need to evaluate and reform large long-standing CHW programmes in sub-Saharan Africa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".