The development and psychometric properties of a new scale to measure mental illness related stigma by health care providers: The opening minds scale for Health Care Providers (OMS-HC)
Bibliographic record
Abstract
BACKGROUND: Research on the attitudes of health care providers towards people with mental illness has repeatedly shown that they may be stigmatizing. Many scales used to measure attitudes towards people with mental illness that exist today are not adequate because they do not have items that relate specifically to the role of the health care provider. METHODS: We developed and tested a new scale called the Opening Minds Scale for Health Care Providers (OMS-HC). After item-pool generation, stakeholder consultations and content validation, focus groups were held with 64 health care providers/trainees and six people with lived experience of mental illness to develop the scale. The OMS-HC was then tested with 787 health care providers/trainees across Canada to determine its psychometric properties. RESULTS: The initial testing OMS-HC scale showed good internal consistency, Cronbach's alpha = 0.82 and satisfactory test-retest reliability, intraclass correlation = 0.66 (95% CI 0.54 to 0.75). The OMC-HC was only weakly correlated with social desirability, indicating that the social desirability bias was not likely to be a major determinant of OMS-HC scores. A factor analysis favoured a two-factor structure which accounted for 45% of the variance using 12 of the 20 items tested. CONCLUSIONS: The OMS-HC provides a good starting point for further validation as well as a tool that could be used in the evaluation of programs aimed at reducing mental illness related stigma by health care providers. The OMS-HC incorporates various dimensions of stigma with a modest number of items that can be used with busy health care providers.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".