Clinical and ethical dimensions of an innovative approach for treating mental illness: a qualitative study of health care trainee perspectives on deep brain stimulation.
Bibliographic record
Abstract
BACKGROUND: The acquisition of knowledge and application of critical thinking skills are required to tackle the clinical and ethical dimensions of new approaches and technologies. Health care trainees rely partly on their training to manage, reason and reflect on the ethical uncertainties of innovations and new technologies. Deep brain stimulation (DBS) is neurosurgery involving the implantation of electrodes into deep brain nuclei and is approved for Parkinson's disease and other motor disorders. Experimental uses of DBS are emerging in refractory obsessive compulsive disorder and depression. METHODS: We conducted a qualitative interview-based study to gather the perspectives of health care trainees from different disciplines on the clinical and ethical issues associated with DBS in psychiatric disorders. RESULTS: First impressions about the use of DBS in mental illness were mixed. We identified factors influencing impressions about DBS and information missing that compounded uncertainty about long-term outcomes and effects on other physical or psychological systems. Participants revealed nascent exploration of the ethical issues of DBS. They emphasized the obligations of health care providers to manage ethical problems and supported patient autonomy in guiding choice, even when choosing innovative approaches. DISCUSSION: We discuss trainee expectations about evidence in decision making and the role of ethics education.
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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 | 0.033 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".