A Hospital Based Cross Sectional Study on Dietary Status and Associated Factors among People Living with HIV/AIDS in Kigali, Rwanda
Bibliographic record
Abstract
Background Good nutrition empowers PLWH with the ability to fight against infection ultimately slowing down disease progression. Consequently, nutrition management is a crucial component of HIV treatment, care, and support. This study aimed at assessing dietary status and associated factors among PLWH in Kigali, Rwanda. Methods We conducted a cross sectional study in three selected hospitals in Kigali from over a six-week period in July – August, 2019 to collect data from 204 HIV positive adults enrolled using systematic random sampling. Data was collected using an adapted, validated and pre-tested food frequency questionnaire (FFQ). Descriptive and multiple logistic regression analyses were performed using SPSS version 25 for windows. Results The proportion of participants with poor dietary status was 15% based on FFQ responses. The study found only three factors to be independently associated with dietary status. There was an association between dietary status and HIV status disclosure (AOR 2.5; CI 1.25 - 4.83; p=0.014). There was an association between dietary status and travel time to place of collection of ARVs (AOR 3.2; CI 1.7 - 5.8; p=0.006). There was an association between dietary status and BMI (AOR 10.2; CI 8.30 – 16.0; p<0.001). Conclusions Poor dietary status among PLWH remains a concern. The strong association between dietary status and BMI underlines the need for interventions that target PLWH to improve dietary status and ultimately nutrition status
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".