Social Assistive Robots for Assisting Activity Professionals
Bibliographic record
Abstract
Abstract Activity Professionals have high expectations for creating engaging and active resident social programming. A socially assistive robot (SAR) specifically designed for community-based settings has the potential to improve social programming. A SAR is suitable for engagement during times with social contact is restricted, such as COVID-19, other infectious outbreaks, weak immune system, or inability to move. We conducted an online survey to determine how a SAR can best support the responsibilities of Activity Professionals. Activity Professionals (N=19) completed the online questionnaire. Respondents (aged M=48.00, SD=12.87; 95% female, 100% native English speakers, 68% White/Caucasian, 21% Black/African American) were highly educated/experienced: 68% had a Bachelor’s degree or above, and 53% had 10-35 years of experience. Respondents worked in Independent Living (68%), Assisted Living (37%), Memory Care (26%), Skilled Nursing (21%), or Personal Care (11%). Respondents rated their job as very demanding (8 out of 10). Differences existed in terms of physical and temporal demands. Job satisfaction was high (average 8 out of 10; SD= 2). Respondents reported enjoyment in preparing, personalizing, and running activities. Least preferred was gathering residents for activities. Respondents wanted more help, but it depended on the task. Qualitative data analysis showed that help was desired for motivating residents to join activities, group communication, and resident devices. A SAR, equipped with the ability to reach every resident’s living quarter, has the potential to provide group communication, deliver engagement programs, and motivate residents to join events, providing Activity Professionals more time to engage with residents for more personal interaction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".