Clinical medical practice and stigma towards patients with substance use disorder in an Italian sample of healthcare workers.
Bibliographic record
Abstract
INTRODUCTION: People with substance use disorder (SUD) face challenges like stigma and discrimination, impacting their healthcare experiences. AIM: This study aims to: (i) assess physicians' clinical practices and stigma toward SUD patients among healthcare personnel and (ii) explore the relationship among stigma, psychological well-being, and burnout. METHODS: A survey covering sociodemographic data, physicians' clinical practices, stigmatizing attitudes, psychological well-being, and burnout was completed by 1,796 employees of the Veneto's Local Health Units (Italy). RESULTS: Healthcare professionals reported increased stigma towards SUDs (p-values<0.05). Stigma consistently correlated with variables such as sex, profession, department, and levels of burnout (p-values<0.05). Notably, high burnout levels were associated with increased stigma. Staff in addiction departments displayed lower stigma levels compared to other departments. No significant differences were found in physicians' clinical practices. CONCLUSIONS: Targeted training for healthcare professionals is crucial to reduce stigma, enhance attitudes toward SUDs, and broaden overall knowledge of the condition.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".