Leaving patients to their own devices? Smart technology, safety and therapeutic relationships
Bibliographic record
Abstract
BACKGROUND: This debate article explores how smart technologies may create a double-edged sword for patient safety and effective therapeutic relationships. Increasing utilization of health monitoring devices by patients will likely become an important aspect of self-care and preventive medicine. It may also help to enhance accurate symptom reports, diagnoses, and prompt referral to specialist care where appropriate. However, the development, marketing, and use of such technology raise significant ethical implications for therapeutic relationships and patient safety. MAIN TEXT: Drawing on lessons learned from other direct-to-consumer health products such as genetic testing, this article explores how smart technology can also pose regulatory challenges and encourage overutilization of healthcare services. In order for smart technology to promote safer care and effective therapeutic encounters, the technology and its utilization must be safe. CONCLUSION: This article argues for unified regulatory guidelines and better education for both healthcare providers and patients regarding the benefits and risks of these devices.
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 | 0.016 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.013 | 0.010 |
| 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".