Exploration of student nurses’ perceptions towards individuals with opioid use disorders in Scotland: A mixed method investigation
Bibliographic record
Abstract
Introduction: Worldwide, an opioid epidemic continues to escalate. While the scientific community has recognized substance use disorders as a biophysiological disease, society continues to view addiction as a social problem and not a medical one. Individuals with opioid use disorders have been stigmatized and negatively characterized as morally weak and defective. Previous studies reveal that these negative attitudes often prevail among nurses and that nurses report dissatisfaction and a lack of education preparation to care for this increasing worldwide population. While studies have been conducted in countries with high incidences of opioid deaths, Scotland, a country faced with significantly high opioid related deaths, has not investigated student nurses’ perceptions of individuals with opioid use disorders. Purpose and Design: This mixed-method explanatory sequential investigation sought to explore pre-registration nursing students’ knowledge, attitudes and stigma towards individuals with opioid use disorders in Scotland. The objectives of this study were to measure pre-registration nursing students’ knowledge, attitudes, and stigma towards individuals with opioid use disorders and explore relationships among any variables. Results/Conclusion: Study participants demonstrated the need for increased knowledge, and improved attitudes towards individuals with opioid use disorders. While stigma was evident, the qualitative findings showcased that participants were empathic, compassionate, non-judgmental and willing to care for individuals with opioid use disorders. Further inquiry should explore the role of empathy-based training and experience with individuals with opioid use disorders to reduce mortality and morbidity in this escalating population worldwide.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".