Adapting the Opening Minds Stigma Scale for Healthcare Providers to Measure Opioid-Related Stigma
Bibliographic record
Abstract
The opioid crisis in Canada continues to cause a devastating number of deaths. Community-based naloxone programs have been identified as one of the solutions for combatting this crisis; however, there are disparities in which pharmacies stock and offer naloxone. Opioid-related stigma is a major barrier for limited naloxone distribution through pharmacies. Therefore, the development of anti-stigma interventions is crucial to improve naloxone distribution in Canada. However, there is no validated tool to specifically measure opioid-related stigma. The Opening Minds Stigma Scale for Healthcare Providers (OMS-HC) is a validated scale used to measure mental illness-related stigma. This study will adapt the OMS-HC by using four different opioid-related terminologies to determine which is the most stigmatizing to use in an opioid-related anti-stigma intervention. Pharmacy students completed four versions of the adapted OMS-HC. The average OMS-HC scores and Cronbach's α co-efficient were calculated for each version. The term "opioid addiction" was found to be the most stigmatizing term among participants and will be used in the adapted version of the OMS-HC in a future anti-stigma interventions.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".