Opening Minds Stigma Scale for Health Care Providers (OMS-HC): Examination of psychometric properties and responsiveness
Bibliographic record
Abstract
BACKGROUND: Diminishing stigmatization for those with mental illnesses by health care providers (HCPs) is becoming a priority for programming and policy, as well as research. In order to be successful, we must accurately measure stigmatizing attitudes and behaviours among HCPs. The Opening Minds Stigma Scale for Health Care Providers (OMS-HC) was developed to measure stigma in HCP populations. In this study we revisit the factor structure and the responsiveness of the OMS-HC in a larger, more representative sample of HCPs that are more likely to be targets for anti-stigma interventions. METHODS: Baseline data were collected from HCPs (n = 1,523) during 12 different anti-stigma interventions across Canada. The majority of HCPs were women (77.4%) and were either physicians (MDs) (41.5%), nurses (17.0%), medical students (13.4%), or students in allied health programs (14.0%). Exploratory factor analysis (EFA) was conducted using complete pre-test (n = 1,305) survey data and responsiveness to change analyses was examined with pre and post matched data (n = 803). The internal consistency of the OMS-HC scale and subscales was evaluated using the Cronbach's alpha coefficient. The scale's sensitivity to change was examined using paired t-tests, effect sizes (Cohen's d), and standardized response means (SRM). RESULTS: The EFA favored a 3-factor structure which accounted for 45.3% of the variance using 15 of 20 items. The overall internal consistency for the 15-item scale (α = 0.79) and three subscales (α = 0.67 to 0.68) was acceptable. Subgroup analysis showed the internal consistency was satisfactory across HCP groups including physicians and nurses (α = 0.66 to 0.78). Evidence for the scale's responsiveness to change occurred across multiple samples, including student-targeted interventions and workshops for practicing HCPs. The Social Distance subscale had the weakest level of responsiveness (SRM ≤ 0.50) whereas the more attitudinal-based items comprising the Attitude (SRM ≤ 0.91) and Disclosure and Help-seeking (SRM ≤ 0.68) subscales had stronger responsiveness. CONCLUSIONS: The OMS-HC has shown to have acceptable internal consistency and has been successful in detecting positive changes in various anti-stigma interventions. Our results support the use of a 15-item scale, with the calculation of three sub scores for Attitude, Disclosure and Help-seeking, and Social Distance.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".