A Living Ethics Project to Address Psychological Distress in Chronic Illness: Process and Outcomes
Bibliographic record
Abstract
INTRODUCTION: Individuals living with a complex or rare chronic disease live a life where the use of healthcare services and self-care are part of their quotidian and even of their identity. They may, as a result, experience significant psychological distress. Yet, specialized healthcare providers (HCPs) who manage their care are often ill-equipped to respond to the emotional or social dimensions of their patients' illness and intervene in a meaningful way. METHODS: In this paper, we report on the process and outcomes of a living ethics project, structured as a living lab. The living lab followed a five-phase methodology, each phase involving various research methods (e.g., semi-structured interviews, group interviews) and oriented toward distinct tasks: identifying the issue (phase 1: problem identification), deepening understanding (phase 2: problematization), co-developing interventions (phase 3: ideation), implementing them (phase 4: enactment), and evaluating the interventions and the overall process (phase 5: evaluation). RESULTS: Phase 1 led to the identification of neglected psychological distress of patients as an important ethical issue. Phase 2 exhibited causes and consequences of psychological distress. Phase 3 led to the co-development of: (1) an electronic medical appointment preparation form for patients, aimed at guiding medical consultations based on their specific needs, facilitating communication, and opening discussions about mental health; (2) a directory of mental health resources intended for clinic staff to better equip them in addressing the mental health of patients; and (3) mental health awareness posters with catchy slogans strategically placed throughout the clinic to raise awareness about mental health and encourage open discussions. Phase 4 led to the implementation of these interventions and phase 5 to their evaluation. All interventions were evaluated positively as well as the participatory nature of the research project while many core aspects of living ethics were furthered. CONCLUSION: This project shows that directly engaging stakeholders in ethics research, by addressing the moral issues they deem significant and working with them to tackle those issues rather than conducting research on them, can lead to tangible, unexpected, and positive moral and clinical outcomes, even within a short timeframe and with limited resources.
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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 | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".