A psychometric evaluation of the Opening Minds Scale for Workplace Attitudes (OMS-WA), a measure of public stigma towards mental illnesses in the workplace
Bibliographic record
Abstract
The anti-stigma Opening Minds initiative (Mental Health Commission of Canada) identified the workplace as one of four critical areas for stigma evaluation and programming. Researchers found, however, that no validated tool to measure stigma in this context existed at the time. The purpose of the current research was to develop and assess the psychometric properties of a multi-component quantitative measure of public stigma towards mental illnesses in the workplace: Opening Minds Scale for Workplace Attitudes (OMS-WA). Three studies are discussed. Study 1 outlines the initial development of the OMS-WA, finding a five-factor structure with 23 items (EFA analysis; N = 207). Study 2 utilized employees from various businesses to further validate the OMS-WA , ( N = 107), finding good internal consistency and good convergent/divergent validity. Study 3 utilized baseline data from two national large-scale stigma reduction and resiliency programs (first responders and general workplaces) to confirm the factor structure, finding good model fit and reliability with both samples ( N = 5385; N = 1207). Together, these results provide strong evidence of the OMS-WA . Its use in measuring workplace stigma related to mental illness is recommended. Considerable future evaluation is warranted, and directions for generalizability studies, as well as suggestions for using the OMS-WA in intervention trials, are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 |
| 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 | 0.000 | 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".