Usability of simplified audiometry and electrocardiogram during treatment of drug-resistant tuberculosis in Mozambique: a qualitative study
Bibliographic record
Abstract
BACKGROUND: In 2021, there were approximately 450,000 cases of drug-resistant tuberculosis (DR-TB) worldwide. The treatment of DR-TB historically included expensive and toxic injectable drugs leading to adverse effects including ototoxicity and Electrocardiogram (ECG) abnormalities. This study described the perspectives of healthcare providers and people with DR-TB on the usability of simplified audiometry and ECG for monitoring treatment adverse effects. METHODS: A qualitative study was conducted in December 2019 across four provinces in Mozambique, namely Maputo, Gaza, Zambézia, and Nampula. Sixteen outpatient primary care health facilities equipped with simplified Audiometry and/or ECG devices (specifically, SHOEBOX Audiometer® and/or SmartHeart Pro ECG®) installed for at least 6 months before the study initiation were selected. The data was collected using in-depth interviews (IDI) and Focus Group Discussions (FGD) techniques. Interviews were audio-recorded, transcribed verbatim in Portuguese, coded, and analyzed using Nvivo 12 software®. We generated two themes and fit our results into a conceptual framework consisting of three domains in the implementation of technological innovations in health. RESULTS: A total of 16 healthcare providers and 91 people undergoing treatment for DR-TB were enrolled in the study. Most people with DR-TB had experienced audiometry testing and demonstrated a good understanding of the assessments. Conversely, while most healthcare providers demonstrated robust knowledge of the importance of both audiometry and ECG assessments, they were not confident in managing ECG devices and interpreting the results. CONCLUSIONS: While healthcare providers demonstrated a consolidated understanding of the importance of audiometry, the limited number of devices and lack of training were constraints, impeding optimal usage and service delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".