Clinical guidelines for managing hearing loss as a complication of drug-resistant tuberculosis treatment: an evaluation of implementation fidelity in Kano, Nigeria
Bibliographic record
Abstract
BACKGROUND: Nigeria has a high burden of Tuberculosis (TB) including Drug-resistant Tuberculosis (DR-TB) and hearing loss. Despite several efforts directed toward its control, many patients fail to respond to treatment, having developed DR-TB. Lack of adherence to the DR-TB guidelines/improper implementation of the guideline has been identified as one of the factors impeding on effective treatment. This study sought to measure the implementation fidelity of health workers to management guidelines for hearing loss resulting from DR-TB treatment and to identify its determinants. METHOD: A questionnaire-based cross-sectional study was conducted at the Infectious Disease Hospital, Kano. Implementation fidelity of the Programmatic Management guidelines for the treatment of Drug-resistant Tuberculosis was measured under the four domains of content, coverage, duration and frequency. The determinants examined are intervention complexity, facilitation strategies, quality of delivery and participant responsiveness as proposed by the Carroll et al. framework. Other determinants used are age, sex, professional cadre and work experience of healthcare providers. RESULTS: The Implementation fidelity score ranged from 40 to 64% with a mean of 47.6%. Quality of delivery, intervention complexity, participants' responsiveness, and being a medical doctor exerted a positive effect on implementation fidelity while facilitation strategy, age and work experience exerted a negative effect on implementation fidelity. CONCLUSION: The implementation fidelity of management guidelines for hearing loss resulting from DR-TB treatment was low. Implementation fidelity should be assessed early and at intervals in the course of implementing the Programmatic Management of Drug-resistant Tuberculosis guideline and indeed, in the implementation of any intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".