Managing Substance Use Disorders and Addiction: How to Integrate Professionalism and Ethics with the Complexity of Diagnosis and Therapy
Bibliographic record
Abstract
Physicians and other healthcare staff who work with the patients having substance use disorders (SUDs) and addiction have to deal with different and complex scenario invariably in comparison to other healthcare providers dealing with regular medical services sought by the patients. Hence, the unconventional nature of practice regarding patients of SUDs and addiction and the diverse range of backgrounds among substance use workers, e.g., psychiatrist, general physician, nurse, psychotherapist, counsellor, social worker, and public health specialist highlights the universal relevance of being more professional and more ethical in practice. They are expected to demonstrate a variety of ethical and professional competences to safeguard themselves and others involved. Clinical team engaged in such activities requires specific ethical training that can help them face specific ethical dilemmas and guide through deeper consideration of those pressing and complex issues, using specific framework, professional guidelines, or institutional management approaches. Professional societies and accrediting institutions should apply their authority to establish practice standards, competencies, regulatory procedures, and codes of ethics to help guide practice and protect public trust and confidence. For the team involved in practice, it is important to become familiar with and adhere to the principles and values that define professionalism and ethical conduct in care for patients suffering from SUDs and addiction. The review paper aims to examine the intersection of professionalism and ethics with the complexity of diagnosis and therapy in this specific area of practice. Besides, some frameworks will be highlighted which may help healthcare providers to foster ethical decision making. It is also a modest effort to show some new insights for improving the quality of care in addiction and mental health by the professional team and healthcare organization from both clinical and operational viewpoint. Mugda Med Coll J. 2024; 7(1): 42-47
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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 | 0.028 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".