Das Inventar Subjektiver Stigmaerfahrungen (ISE): Ein neues Instrument zur quantitativen Erfassung subjektiven Stigmas
Bibliographic record
Abstract
OBJECTIVES: Stigma research has been criticized for excluding the views of those exposed to stigmatizing reactions. While this changed with the advent of qualitative research into stigma, population-based data on the prevalence, severity and consequences of stigma experiences are lacking. The present study aims at field-testing the German version of the Inventory of Stigmatizing Experiences (ISE) , developed to investigate the epidemiology of "felt stigma". METHODS: The ISE is a semi-structured questionnaire composed of two scales: one measuring the scope of stigma experienced in different life domains (SES; 9 items), the other assessing their psychosocial impact (SIS; 7 items). The instrument was translated into German, using the three-step procedure proposed by WHO (translation, back-translation, feasibility testing). Field-testing of the German version was carried out on 95 service users. RESULTS: The German version of the ISE shows good reliabilities for both the stigma experiences (SES; alpha = 0.74) and stigma impact (SIS; alpha = 0.86) scales. 54.4 % of service users report stigma experiences across the SES; 72 % of these are recent and thus susceptible for interventions. CONCLUSIONS: The German version of the ISE is a compact and reliable tool for measuring the prevalence and impact of felt stigma, with potential uses in both population-based stigma research and clinical practice.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".