Bibliographic record
Abstract
Health is usually seen as an important prerequisite for the realization of life goals and therefore has a great meaning in society. Many authors and their perspectives also make it clear that health can be seen as a moral value that is ethically relevant and must be promoted. In recent years, numerous crises, armed conflicts, digitalization and, more generally, the fast pace of life in society, have contributed to raise awareness of mental health. This article deals with an ethical analysis of mental health in the context of AI-based care robots. Robot companions in the care sector are increasingly being used, and this trend will continue in the near future. However, the question arises as to what extent these machines can contribute to mental health when interacting with people receiving care. First, the relevance of mental health and ethical implications are presented. In a second step, care robots and their potential influence on the mental health of individuals in need of care are discussed. The third step shows how fair access to the value of (mental) health can be realized, even and perhaps because care robots are increasingly assigned to care for people. Finally, ethical challenges are discussed, and possible objections are addressed. The focus is ultimately on the importance of care robots, since they can address the issue of mental health, at least to some extent, in a specific technical way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".