Psychometric properties of a Spanish version of the 10-item Berger's stigma scale in Colombia: a validation study
Bibliographic record
Abstract
Introduction: HIV-related stigma is detrimental to people living with HIV (PLH), and reducing it is essential for achieving an HIV/AIDS-free generation. Abbreviated stigma scales can improve the feasibility of surveys that broadly explore factors affecting PLH. This study tested the psychometric properties of a Spanish translation of the abbreviated 10-item Berger's HIV stigma scale. Methods: We recruited a sample of 105 PLH regularly attending a specialized clinic in Cali, Colombia. English-to-Spanish and Spanish-to-English back translation was performed of the Berger's 10-item HIV stigma scale. Exploratory and confirmatory factor analyses were carried out to assess its validity. Pre- and post-test reliability (15 days) was estimated with the intra-class correlation coefficient (ICC). Results: The Confirmatory Factor Analysis (CFA) was used to confirm a two-factor solution with three poor items removed, resulting in a 7-item HIV Stigma Scale. The resulting 7-item HIV stigma scale had a Cronbach's alpha of 0.73 with an ICC of 0.83 (CI 95%: 0.75–0.89). One factor loaded three items related to negative self-image (internalised stigma), and the other four items were related to personalized (enacted) HIV stigma. Both factors were related to depression and adherence to antiretroviral therapy. Conclusion: The Spanish translation of the 10-item HIV stigma scale did not perform well due to problems in items 4, 5, and 6. Rather, a modified 7-item version had a good fit with a two-factor loading in which both HIV stigma factors correlated significantly with depression and HIV medication adherence.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".