Nutrition knowledge, attitude, practice and assessment of nutritional status of tuberculosis patients attending selected DOTS clinics in Lagos State, Nigeria
Bibliographic record
Abstract
Background: Nutritional status of tuberculosis (TB) patients largely influences treatment outcomes, thereby determining the success of tuberculosis control programmes.Objective: The study is to assess the nutrition knowledge, attitude and practice and the nutritional status of TB patients attending selected DOTS clinics in Lagos State, South - west, Nigeria.Methods: A descriptive cross-sectional study was conducted among 400 diagnosed TB patients attending DOTS clinics in some state hospitals (secondary facilities) in Lagos State, using a pre-tested, semi-structured interviewer-administered questionnaire. Heamoglobin (Hb) and albumin levels were determined in about a quarter of the respondents. The data obtained from the questionnaire survey was analyzed using the Epi-info software version 3.5.1.Results: Findings revealed that only one quarter (25%) of the respondents had good knowledge of nutrition, approximately 9 out of 10 (86.8%) of them had positive attitude towards proper nutrition and almost three quarters (71.8%) had good nutrition practices. Using the Body Mass Index (BMI), about one third (35%) of the respondents were undernourished with 47.1%, 27.1% and 25.7% of the under nourished TB patients having mild, moderate and severe under nutrition respectively.Out of the quarter (100) respondents who had biochemical nutrition assessment, 43% were anaemic (Hb<12g/dl) and only 22% had hypoalbuminaemia. Explored factors influencing knowledge, attitude and practice of nutrition were: educational level of respondents which significantly (p=0.0006) influenced their knowledge of nutrition, attitude to nutrition was significantly influenced by employment status (p=0.004), household size (p=0.000) and knowledge (p=0.032). Nutritional practices were influenced by educational level (p=0.04), household size (p=0.043) and attitude (p=0.021) to nutrition.Conclusion: To effectively improve treatment outcomes of TB patients and reduce the burden of the disease, it is important to improve patients' knowledge, attitude and practice of good nutrition through nutrition education during therapy, provision of high nutrient supplements, ensuring regular availability of anti-TB drugs and compliance with treatment.Keywords: Tuberculosis, Nutritional assessment, Knowledge, Attitude and Practice
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".