Experience of fatigue and related factors among HIV-infected adults attending ART clinic in Ethiopia; a cross-sectional study
Bibliographic record
Abstract
Abstract Background: Fatigue is one of the most common bothersome HIV-related morbidity. The HIV prevalence in Ethiopia is heterogeneous by sex, geographic areas, and population groups. In Ethiopia, there is a need to estimate the burden of fatigue among HIV/Acquired Immune Deficiency Syndrome (AIDS) adults to gain regional insight into this disabling symptom.Method: An institutional-based cross-sectional study was conducted among 392 HIV/AIDS patients attending an antiretroviral therapy clinic at the University of Gondar Hospital, Ethiopia using a systematic random sampling technique. Data were collected using a structured questionnaire, nine-item version Fatigue Severity Scale (FSS), and PHQ-9 (Patients Health Questionnaire 9). Logistic regression model was used to identify factors associated with the reported presence of fatigue.Result: The mean age of the participants was 40.5 ± 8.5 years. The prevalence of HIV-related fatigue was 53.3% and about 66% of HIV-infected women experienced fatigue. The factors associated with fatigue experience were; female gender (AOR: 0.196, 95%CI; 0.05, 0.92), being married (AOR: 0.13, 95% CI 0.23, 0.7), low income (AOR: 12.3, 95% CI 2.5, 60.15), unemployed (AOR: 3.9, 95%CI (1.02, 14.739), parity (AOR: 7.99, 95% CI 1.66, 38.41), being anemia (AOR: 13.34, 95% CI 2.74, 65.01), mild weight loss (AOR: 4.9 95% CI 4.33, 19.5) and moderate weight loss (AOR: 5.5 95% CI 3.11, 21.3), respectively.Conclusion: The findings of this study revealed that experiencing fatigue is quite high among adults living with HIV. It is important for health care professionals and people living with HIV to understand; the possible causes of fatigue, remedies, and ways to reclaim energy. The predisposing factors and complications that cause fatigue should be aggressively diagnosed and treated by the clinicians.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".