Measuring Stigma Towards People with Opioid Use Problems: Exploratory and Confirmatory Factor Analysis of the Opening Minds Provider Attitudes Towards Opioid-Use Scale (OM-PATOS)
Bibliographic record
Abstract
Many countries are experiencing an ongoing opioid crisis characterized by high rates of opioid use problems, overdose, poisoning, and death. Stigma has been identified as a central problem for seeking and receiving quality services from health providers and first respondents. The Mental Health Commission of Canada developed a scale that could be used to measure stigma in this population, as no such scale currently exists. This paper provides the results of psychometric testing of this new scale, known as the Opening Minds Provider Attitudes Towards Opioid-Use Scale (OM-PATOS), using exploratory (EFA) and confirmatory (CFA) factor analysis. EFA findings showed a 15 item 2-factor solution, with subscales of 'attitudes' (6 items) and 'behaviours/motivation to help' (9 items). The confirmatory factor analysis provided some preliminary confirmation of the factor structure suggested by the exploratory analyses, but further research with larger samples is needed to fully confirm the factor structure. Overall, results support the use of the 15-item scale with health professionals and first responders, with factors used for descriptive value rather than as calculated subscales until further research can be completed.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".