Knowledge, attitudes and practices on tuberculosis transmission and prevention among auxiliary healthcare professionals in three Brazilian high-burden cities: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Tuberculosis elimination requires treatment of latently infected high-risk persons, such as contacts of index cases. Identification and referral of tuberculosis contacts for investigation are major barriers in the contact cascade-of-care. These tasks rely heavily on auxiliary primary healthcare workers in many low- and middle-income countries. We aimed to understand their knowledge, attitudes and practices (KAP) regarding contact investigation in Brazil. METHODS: We conducted a cross-sectional KAP survey on tuberculosis transmission and prevention among 135 auxiliary healthcare workers in three tuberculosis high-burden Brazilian cities. Trained interviewers applied a translated version of a previously applied questionnaire. Open answers were classified in pre-defined objective categories and analysed quantitatively. Answers were further classified as satisfactory or not according to criteria set by a panel of three specialists. RESULTS: Although 66% had received tuberculosis training in the past 10 years, only 19% were trained for tuberculosis prevention. 64% could not clearly distinguish latent tuberculosis infection (LTBI) from active tuberculosis; 63% did not know how to diagnose LTBI and 52% did not know how to prevent progression to active tuberculosis. Most believed that it is important to investigate adult (99%) and child (96%) contacts for LTBI. However, not all invite contacts - children (81%) or adults (71%) - to the clinic, despite only 24% perceiving difficulties for investigation. CONCLUSIONS: Gaps in KAP among auxiliary health workers have been reported in other areas, such as obstetrics and other infectious diseases. To the best of our knowledge, this is the first KAP survey on tuberculosis transmission and prevention among auxiliary health care workers, and relevant gaps were also found. Knowledge gaps were notably related to LTBI management, including how to recognize it and prevent progression to active tuberculosis through treatment, despite most recognizing the importance of investigating contacts. Auxiliary healthcare workers in three Brazilian high-burden cities have important knowledge gaps despite their perception of the importance of tuberculosis prevention among contacts. They need to incorporate contact referral as one of their tasks to enable progress toward the target of tuberculosis elimination.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".