Cardiology professionals’ views of social robots in augmenting heart failure patient care
Bibliographic record
Abstract
Aims: Social robots are arriving to the modern healthcare system. Whether patients with heart failure, a prevalent chronic disease with high health and human costs would derive benefit from a social robot intervention has not been investigated empirically. Diverse healthcare provider's perspectives are needed to develop an acceptable and feasible social robot intervention to be adopted for the clinical benefit of patients with heart failure. Using a qualitative research design, this study investigated healthcare providers' perspectives of social robot use in heart failure patient care. Methods and results: = 22; saturation was reached with this sample; 77% female; 52% physicians) were open to using social robots to augment their practice, particularly with collecting pertinent data and providing patient and family education and self-management prompts, but with limited responsibility for direct patient care. Prior to implementation, providers required robust evidence of: value-added beyond current remote patient monitoring devices, patient and healthcare provider partnerships, streamlined integration into existing practice, and capability of supporting precision medicine goals. Respondents were concerned that social robots did not address and masked broader systemic issues of healthcare access and equity. Conclusion: The adoption of social robots is a viable option to assist in the care of patients with heart failure, albeit in a restricted capacity. The results inform the development of a social robotic intervention for patients with heart failure, including improving social robot efficiencies and increasing their uptake, while protecting patients' and providers' best interest.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".