Launching a Living Ethics Initiative to Explore Patients’ Psychological Distress in a Highly Specialized Interdisciplinary Care Clinic
Bibliographic record
Abstract
Ethical issues are often difficult to discuss openly in clinical settings. However, silence can be detrimental to both patients and healthcare providers, and may impede improvements in clinical practices. In this paper, we report on the initial process of launching a living ethics initiative to identify, explore, and address a relevant ethical issue-that is, unaddressed psychological distress among people living with a rare or complex chronic disease-with the staff and patients of a highly specialized interdisciplinary care clinic in Montreal (Canada). Although previous research has addressed psychological distress, few studies have taken a participatory qualitative research and ethics approach, integrating the perspectives of both patients and healthcare providers. This paper specifically outlines the initial phases of a five-phase living lab project, from identifying the issue to exploring stakeholders' understanding of the problem. Semi-structured interviews were conducted with patients and clinic staff, followed by a qualitative content analysis that relied on deductive and inductive coding strategies. Overall, our study sheds light on the concept of psychological distress, causes of patients' psychological distress, consequences of patients' psychological distress, mitigating factors of patients' psychological distress, management of patients' psychological distress within a given healthcare environment, and potential avenues for improvement. By creating an ethical space where patients and healthcare providers could reflect on and discuss this issue, this initiative has not only deepened our understanding of patients' psychological distress but has also initiated a paradigm shift in this clinical setting, recognizing that patients' psychological distress is a fundamental human issue that concerns everyone.
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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 | 0.044 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".