Examining the Application of the Opening Minds Survey in the Community Health Centre Setting
Bibliographic record
Abstract
OBJECTIVE: Stigma has been identified as a complex and problematic issue. It acts as a major barrier to accessing care and can exacerbate the experience of a health condition, particularly for clients with mental illness and substance use issues. Scales designed to assess stigmatising attitudes towards those with mental illness and substance use problems among health care providers are necessary to evaluate programs designed to reduce that stigma. The goal of this study was to evaluate the internal reliability and external validity of the Opening Minds Survey for Health Care Providers (OMS-HC). METHODS: The current study examined the use of the OMS-HC in assessing stigma held by Community Health Centre (CHC) staff towards clients with mental and/or substance use problems. Participants represented staff from 6 CHCs in the Greater Toronto Area ( n = 190). RESULTS: The OMS-HC was found to have acceptable internal reliability for the 15-item version of the scale (α = 0.766) and mixed reliability for its subscales (α = 0.792-0.673). Confirmatory factor analysis showed good absolute (root mean square error of approximation = 0.013) and relative fit (Tucker-Lewis index = 0.996) for the current data. The OMS-HC was also shown to correlate with a series of scales commonly used in stigma research. CONCLUSIONS: After testing for internal validity and comparing the OMS-HC to other commonly used scales for assessing stigma and attitudes concerning recovery, the scale was found to be appropriate for the CHC setting and may be advantageous over the use of multiple scales.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".