Experiences of patients diagnosed with drug susceptible tuberculosis regarding lost to follow-up in Engela district, Ohangwena region
Bibliographic record
Abstract
Lost to Follow-Up (LTFU) amongst Tuberculosis (TB) patients is referred to as a patient diagnosed with TB who interrupts treatment for two consecutive months or more. LTFU has been cited as a major risk factor for the re-emergence of TB strains resistant to first line anti-tuberculosis drugs. Namibia has been reporting increasing levels of patients LTFU over time, with some districts such as Engela reporting a 10% LTFU in quarter 4 of 2017 and 11% in quarter 1 of 2018 and constantly failing to attain the WHO recommended LTFU of below 5%. Patients diagnosed with drug-susceptible TB and registered for treatment after lost to follow up might have different experiences that can lead to them defaulting on treatment and being lost to follow up. Therefore, it was necessary to conduct a study aimed at exploring and describing the experiences of patients diagnosed with drug-susceptible and registered patients LFTU in Engela District, Ohangwena Region. Qualitative research with exploratory, descriptive and contextual designs were used in this study. The data was collected through in-depth interviews conducted at different sites in Ohangwena Region. A sample of 11 patients diagnosed with drug-susceptible TB and registered as patients LFTU were selected using a purposive sampling technique. The sample size was determined by saturation of data as reflected in repeating themes. Interviews were recorded and field notes were taken during the interview to ensure that all experiences of the participants were captured. The data was analysed using Tesch’s eight steps of coding. The results showed that patients diagnosed with drug-susceptible TB had different experiences that led to the patients being lost to follow up on TB treatment. Some patients experienced physical malaise prior to being diagnosed with TB, while others experienced chest pain. The participants iii became lost to follow up to their TB treatment for various reasons such as a lack of adequate information upon commencement of TB treatment and the importance of adherence to therapy, stigma at work and in the community, alcohol indulgence, a lack of proper nutrition and having travelled far away from the area where they initiated treatment. The study recommends the development of holistic LTFU mitigation strategies/interventions aimed at improving organisational and administrative health system challenges impeding health education delivery to patients and the communities and provision of patient-centred care by health care workers. Further, it is important to look into addressing stigma issues and changing labour policies and laws that disadvantage sick people in the workplace and lead them to default therapy.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".