Prevalence and associated factors of alexithymia among people living with HIV/AIDS in China: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Alexithymia is common and causes serious harm to people living with HIV/AIDS. Therefore, this study aimed to examine its prevalence and associated factors among people living with HIV/AIDS in China. METHODS: A cross-sectional study was conducted in two designated AIDS medical institutions in Harbin, China between January and December 2019. In total, 767 participants completed the 20-item Toronto Alexithymia Scale, the University of California Los Angeles Loneliness short-form, the Patient Health Questionnaire-9, the HIV Treatment Regimen Fatigue Scale, and the Alcohol Use Disorders Identification Test-Consumption. The participants responded to several questions regarding their demographic characteristics, life satisfaction, disease-related economic burden, and their antiretroviral therapy (ART) side effects. Multivariate logistic regression assessed the relationship between alexithymia and associated factors. Odds ratios (OR) and 95% confidence intervals (CI) for OR were calculated. RESULTS: Approximately 36.1% of the participants were classified as having alexithymia. After adjusted age and education, the logistic regression model indicated that disease-related economic burden (OR = 1.477, 95% CI = 1.155-1.888), ART side effects (OR = 1.249, 95% CI = 1.001-1.559), loneliness (OR = 1.166, 95% CI = 1.101-1.236), and HIV treatment regimen fatigue (OR = 1.028, 95% CI = 1.017-1.039) were positively associated with alexithymia. CONCLUSIONS: The mental health problems of people living with HIV/AIDS are essential to understand and deserve attention. Disease-related economic burdens are major associated factors. Multiple actors should provide better services and guarantees for patients.
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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 | 0.001 |
| 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".