Factors Hindering Effective Implementation of Trachoma MDA Programme among Communities in Three Districts in Tanzania
Bibliographic record
Abstract
BACKGROUND: Trachoma is a neglected tropical disease affecting many communities in the low-income countries. This study explored in the Trachoma endemic districts in Tanzania factors that have contributed to poor success in the elimination of trachoma using Mass Drug Administration. METHODS: We reviewed reports from the Neglected Tropical Diseases (NTD) Control Programme on the assessment outcome investigations on trachoma surveillance in Monduli, Mpwapwa and Kalambo districts in Tanzania. The reports contained data collected using interviews and focus group discussions with key informants, District Medical Officers, NTD Programme Coordinators, WASH focal persons, veterinarians; frontline health workers and community drug distributors (CDDs). Quotes from participants were used to generate themes and subthemes. RESULTS: Planning for mass drug administration; regional and district council’s programme coordination; household registration; logistic, supply chain and community perception on Azithromycin emerged as major concerns. CONCLUSION: Participants expressed that planning, logistical, operational issues and socio-cultural factors have compromised successful elimination of Trachoma in the studied communities. It is recommended that the NTDC programme should develop targeted socio-cultural, behavioural, change communication materials for elders, men, women and youth groups and should work closely with partners to conduct situation analysis and develop a long-term strategy to address social norms, misconceptions about trachoma and the medicines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".