Translation and validation of the Short HIV Stigma scale in Brazilian Portuguese
Bibliographic record
Abstract
BACKGROUND: HIV-related stigma, or the degree to which people living with HIV endorse negative stereotypes associated with HIV, is associated with poor continuum of care outcomes. We translated the 12-item Short HIV Stigma scale and evaluated its psychometric properties in a Brazilian context with regard to construct validity and reliability. METHODS: The first step included translation, back-translation, evaluation, peer review, and pre-testing of the Short HIV Sigma scale developed by Reinius et al. (Health Qual Life Outcomes 15(1):115, 2017). The second step involved piloting the scale in three convenience samples of adults recruited online through advertisements on different platforms: Grindr (October/2019) and Hornet (February-March/2020), geospatial network apps for sexual encounters for gay, bisexuals and other men who have sex with men, and social media apps (Facebook and WhatsApp, October/2019). The psychometric evaluation included confirmatory factor analysis, differential item functioning using the Multiple-Indicator Multiple-Cause model, and correlations between subscale scores and antiretroviral treatment use and adherence. Reliability was assessed using Cronbach's alpha, and ordinal alpha and omega from the polychoric correlation matrix. RESULTS: In total, 114, 164, and 1824 participants completed the measure items through Grindr, social media, and Hornet, respectively. We confirmed a 4-factor structure with factors for personalized stigma (3 items), disclosure concerns (3 items), concerns with public attitudes (3 items), and negative self-image (3 items). Small differential item functioning with respect to sample was found for one item ("I feel guilty because I have HIV"), which did not substantively influence estimates of latent factor scores. Grindr and Hornet's participants scored significantly higher than social media participants on all factors except personalized stigma. Higher subscale scores correlated with antiretroviral treatment use among participants from Hornet and with lower treatment adherence in participants from Grindr and Hornet. Reliability as measured by Cronbach's alpha, ordinal alpha and omega were 0.83, 0.88 and 0.93 for the entire scale. DISCUSSION: The Brazilian Portuguese version of the Short HIV Stigma scale had satisfactory psychometric properties with present results suggesting that scores from different samples may be compared without concern that measurement differences substantively influence results though further studies with greater representation of women and heterosexual men are warranted.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".