The Pre-Exposure Prophylaxis (PrEP) Stigma Scale: Preliminary findings from a pilot study.
Bibliographic record
Abstract
Despite being at the cornerstone of current initiatives to curtail the spread of HIV, Pre-Exposure Prophylaxis (PrEP) medication has been slow to proliferate among many "at risk" populations. This is true for men who have sex with other men (MSM), who account for the largest number of new HIV diagnoses in the United States. To try to understand why MSM are not adopting PrEP in greater numbers, the present authors have created a 22-item PrEP Stigma Scale. This paper reports findings for that scale. METHODS: Purposive sampling was used to derive a sample of 273 diverse MSM. Men completed a brief questionnaire inquiring about their awareness of PrEP, willingness to avail themselves of various sources of information about PrEP, perceptions about PrEP-related stigma, and perceptions about obstacles to PrEP use. Cronbach's alpha reliability coefficients were computed for the PrEP Stigma Scale, for the full sample and for key subgroups. Factor analysis was performed to determine whether or not subscales exist. RESULTS: The PrEP Stigma Scale was found to be highly reliable, both in its full version (alpha=0.96) and in its shortened version (alpha=0.95). Reliability estimates were strong for all subgroups based on age, race, sexual orientation, educational attainment, relationship status, and HIV serostatus. Two subscales were identified, each with excellent reliability (alpha=0.95 and 0.94), again for the sample as a whole and for all key subgroups. CONCLUSIONS: The PrEP Stigma Scale shows great promise for aiding our understanding of why more MSM are not adopting PrEP. It was found to be reliable for all key subgroups under examination, and that is true both for the 22-item and the 11-item version of the scale.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".