The construction and validation of a new Hepatitis C Virus social stigma in the workplace scale
Bibliographic record
Abstract
Aim The purpose of this study was to develop a new scale to measure the social stigma of hepatitis C virus (HCV) in the workplace using a refined version of Link and colleagues’ (Link & Phelan, 2001, https://doi.org/10.1146/annurev.soc.27.1.363 ; Link, Yang, Phelan, & Collins, 2004, https://doi.org/10.1093/oxfordjournals.schbul.a007098 ) definition of social stigma. Methods The new scale was developed over a multistage process that was guided by existing scales and focus groups. Two studies were conducted to validate the scale. The first, collected data from 224 employees and used exploratory factor analysis to remove unsatisfactory items. The second study collected data from 254 employees and used confirmatory factor analysis. Results Results indicated that the new 32 item scale had acceptable reliability and validity. These results support the conceptualization of stigma as a latent construct that abstracts stereotyping, prejudice and intention to discriminate. Conclusion Findings elucidate that HCV stigma can be operationalized as a general factor behind stereotyping, prejudice and intentions to discriminate in the workplace. This is an important conclusion because it may bring parsimony and coherence to a complex and dispersed literature. Additionally, the new scale may be used to study HCV stigma in the workplace.
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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 | 0.031 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".