Environmental Cognitive Remediation in Schizophrenia: Ethical Implications of “Smart Home” Technology
Bibliographic record
Abstract
OBJECTIVE: In light of the advent of new technologies, we proposed to reexamine certain challenges posed by cognitive remediation and social reintegration (that is, deinstitutionalization) of patients with severe and persistent mental disorders. METHOD: We reviewed literature on cognition, remediation, smart homes, as well as on objects and utilities, using medical and computer science electronic library and Internet searches. RESULTS: These technologies provide solutions for disabled persons with respect to care delivery, workload reduction, and socialization. Examples include home support, video conferencing, remote monitoring of medical parameters through sensors, teledetection of critical situations (for example, a fall or malaise), measures of daily living activities, and help with tasks of daily living. One of the key concepts unifying all these technologies is the health-smart home. We present the notion of the health-smart home in general and then examine it more specifically in relation to schizophrenia. CONCLUSION: Management of people with schizophrenia with cognitive deficits who are being rehabilitated in the community can be improved with the use of technology; however, such technology has ethical ramifications.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".