Measuring aspects of stigma cultures in healthcare settings
Bibliographic record
Abstract
PURPOSE: Stigma cultures in healthcare settings are the organizational-level norms values, assumptions, physical façades, and practices that govern day to day activities and interactions. Aspects include poor quality of care, coercive care, a punitive and patronizing atmosphere, and disempowerment to make treatment decisions. To evaluate the effectiveness of interventions to reduce stigma cultures in healthcare settings, valid and reliable measures are needed. This paper describes the development and preliminary testing of a measure to assess mental illness related stigma in healthcare cultures from the perspectives of service users. METHODS: Item generation was grounded in the lived experiences of people with a mental or substance use disorder (n = 20) reflecting their personal experiences with physical or mental healthcare encounters. Wherever possible, items were adapted from existing scales. Items were rated on a 4-point agreement scale with higher scores indicating higher stigma. Following the qualitative analysis, survey data (n = 2,476) were collected and exploratory and confirmatory factor analysis on split halves of the sample were conducted. RESULTS: The analyses provided statistical support for a 23-item unidimensional scale that could be used in any healthcare setting to assess key aspects of stigma cultures such as poor quality of care or lack of person-centered care. Reliability was high (0.92) and aggregated scale scores (ranging from 0 to 92) were approximately normal. CONCLUSIONS: Though further testing is needed, the resulting Stigma Cultures in Healthcare scale is intended to be used across a range of physical and mental healthcare settings to assess the extent to which key aspects of care are experienced as stigmatizing by clients with mental or substance use disorders.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".