PERCEPTIONS OF OLDER ADULTS AND CAREGIVERS OF AN AUTOMATED ROBOT TO FACILITATE AGING IN PLACE
Bibliographic record
Abstract
Abstract This study explored the perceptions of forty older adults 55+ and caregivers of the usefulness of the Labrador Retriever (https://labradorsystems.com/) in facilitating aging in place. Participants consisted of older adults who are enrolled in a PACE program or reside at The Village of Oakland Woods in southeastern Michigan as well as providers and board members of organizations that provide support to these older adults. Participants viewed an in-person demonstration of the Labrador Retriever before verbally completing a thirteen question survey with answers input by research team members. The questions were a combination of multiple choice questions such as ”Having seen the Labrador demonstration, do you think the Labrador Retriever System could help you in your setting?” followed by open ended questions such as “Having seen the Labrador Retriever, how might this system help you in your setting?”. A total of 9 qualitative themes related to the potential uses of the Labrador Retriever System were identified as: Medications, Aging in Place, Safety and Fall Prevention, Carrying Items (i.e. laundry, food, cleaning supplies), Hydration, Independence, Reducing Caregiver Burden, Nutrition, and Cognitive Issues. These themes are consistent with the barriers to aging in place identified in the literature.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".