Balancing Obligation and Autonomy: A Chatbot for Family Caregivers Facing Life-Sustaining Treatment Dilemmas
Bibliographic record
Abstract
Abstract Family caregivers often expend excessive energy balancing care, personal life, and emotional stability, especially when illness limits the patient’s autonomy and intensifies ethical dilemmas surrounding the caregiver’s decision making. These dilemmas intensify in the absence of caregiver centered communication and support programs. This project seeks to fill the gap by proposing a self-reflective guiding AI chatbot designed to offer early stage emotional, ethical reflection and informative data before treatment decisions are made. This project builds on an ethical analysis framework that combines principal bioethics to address the moral and emotional costs of choosing life-sustaining treatment. Recognizing the need for early-stage emotional support before a decision is made, we propose a self-reflective AI chatbot prototype to help caregivers articulate their values, emotional reasoning, and potential areas of internal conflict. The chatbot includes a DASS-21-based emotional check-in, CBT-informed narrative prompts, and live-updating local welfare resource information when financial strain is detected. All interactions are anonymous and non-directive. The methodology includes a psychology-based assessment to measure stress levels, experiences of ethical conflict, and financial pressures. Qualitative research will be applied to explore family caregiver’s ethical and emotional pattern, aiming to reveal social and structural foundations in their decision-making process. This study offers a roadmap through an ethical lens for examining real world treatment decisions on behalf of patients. By addressing these systemic challenges and suggesting an intervention model, the research seeks to reduce the risk of unethical decisions among family caregivers, ultimately safeguarding patient rights.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".