Training Programmes for Tuberculosis Prevention among Urban Slums Residents in Accra, Ghana: A Longitudinal Review
Bibliographic record
Abstract
Urban slums in Accra, Ghana, are at increased risk of tuberculosis (TB) due to overcrowding and poor living conditions. Community Health Workers (CHWs) play a crucial role in TB prevention by educating residents about infection control measures. A comprehensive search was performed using databases such as PubMed and Web of Science. Studies were included if they reported the efficacy of training programmes delivered to CHWs between and , focusing on changes in knowledge, attitudes, and practices related to TB prevention. Analysis revealed that CHW training significantly improved participants' understanding of TB transmission and preventive measures. Specifically, 78% of trained CHWs reported increased confidence in educating residents about cough etiquette post-training compared to baseline levels. CHW training programmes demonstrated effectiveness in enhancing knowledge and attitudes toward TB prevention among urban slum residents in Accra, with long-term adherence observed over a period of six months. Future research should investigate the scalability of these interventions across different settings and evaluate their cost-effectiveness. Policy-makers could consider implementing similar community-based CHW training programmes as part of broader public health strategies to address TB prevention in urban slums. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".