Ethical Issues in Treating Substance Use Disorders: Counselor Perspectives
Bibliographic record
Abstract
Ethical issues arise frequently in the treatment of substance use disorders (SUD). Counselors need guidance to navigate ethical dilemmas but receive limited training in resolving ethical issues. To narrow the gap between the ethical dilemmas counselors face and their training, this qualitative study assessed ethical issues that counselors encounter, how they resolve them, and desired training. We conducted qualitative individual interviews with 20 front-line counselors working in two SUD treatment programs, presenting brief vignettes that depicted the ethics code of the national organization representing SUD counselors. The interviews asked open-ended questions about how counselors dealt with issues and their ideas for future ethics training. All participants had encountered ethical dilemmas. Areas of concern included confidentiality and privacy, mandatory reporting, fairness/equity, client-counselor boundaries, tensions between workplace and client welfare, and meeting clients' complex needs. Ways participants resolved ethical issues included consultations, using direct approaches to resolve ethical dilemmas, and commitment to providing client-centered care. Useful training in the workplace was sparse. Participants expressed needs for ongoing support to resolve workplace ethical dilemmas. Although the importance of ethical issues is widely acknowledged in treating SUD, this study underscores the need for ongoing and interactive training and supervision about ethical issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".