Health Workers’Experience on Directly Observed Treatment Short Courses Strategy to Stop TB Transmission in Khomas Redion, Namibia
Bibliographic record
Abstract
BACKGROUND: Directly Observed Treatment short courses programme is the key strategy on national efforts to end the TB disease by 2035. The aim of this study was to explore experiences of health care workers who care for patients receiving treatment under DOTS strategy at public health facilities in Windhoek, Namibia. METHODS: A qualitative explorative, descriptive research design was employed and a purposive sampling considering diversity was used to select participants who met the inclusion criteria for the study. A semi-structured interview guide was used to collect data. The study was conducted in the Windhoek district of the Khomas region, with a sample of 14 health care workers. Data was analysed by means of content analysis, a process of organizing and integrating narrative, qualitative data according to emerging themes and concepts. RESULTS: One theme emerged from data, which is the experiences of health care workers when attending to patients on DOTS. Participants shared their experiences on DOTS services and as result, shortage of staff, movements of patients from residential address, alcohol abuse and lack of enough food was repeatedly viewed as a barrier to DOTS services. CONCLUSION: To achieve the goal of reduction of TB cases by 95% by 2030, more training on the DOTS is needed for all health care workers.
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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 | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".