PARO as a Biofeedback Medical Device for Mental Health in the COVID-19 Era
Bibliographic record
Abstract
The COVID-19 pandemic is spreading across the globe, and it could take years for society to fully recover. Personal protective equipment (PPE), various hygiene measures, and social distancing have been implemented to reduce “human to human” contact, which is an essential part of outbreak prevention. The pressure of the pandemic combined with decreased communication and social contact have taken a toll on the mental health of many individuals, especially with respect to anxiety and depression. Effective use of robots and technology as a substitute for—or in coordination with—traditional medicine could play a valuable role in reducing psychological distress now more than ever. This paper summarizes the results of a comprehensive review of clinical research on PARO, a therapeutic seal robot, which has been used extensively as a biofeedback medical device and socially assistive robot in the field of mental health. PARO has proven to be an effective and economical non-pharmacological intervention method for both mental and physical well-being during the COVID-19 pandemic. Utilization of PARO during these times has provided more data for consideration and has helped in mitigating the negative stigma surrounding using robots in therapeutic settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".