Family and friend caregiver satisfaction and utility of passive remote monitoring technology utilized by frail home care clients
Bibliographic record
Abstract
Background: Passive remote monitoring technologies (RMT) utilize information gathered from sensors that is transmitted to a caregiver to alert them to a possible incident.There are gaps in our knowledge about the level of satisfaction with passive RMT from the perspective of those who have actual experience using this technology, including family and friend caregivers.This knowledge is important as caregivers are instrumental in determining whether passive RMT will be adopted and utilized over time.In addition, technologies designed for older adults and their caregivers are both helpful and user-friendly.Objective: The aim of this research was to identify the level of satisfaction and utility of passive RMT from the perspective of family and friend caregivers of frail older adults who utilized the technology for a minimum of 90 days.Method: Questionnaire data was collected through interviews with family and friend caregivers of frail home care clients who utilized passive RMT for at least 90 days.Participants could choose from an array of passive RMT sensors that met their needs (e.g., technologies that monitored falls, eating, sleeping, movement/lack of movement, washroom patterns, taking medications, and video cameras).Data reported included closed-ended questionnaires the type of sensors utilized, level of satisfaction with the type of sensor, usability, and satisfaction with passive RMT.Results: Of the 80 participants, most participants were over age 60, female, lived with the home care client, and reported high levels of caregiver burden.Sensors to detect wandering were frequently utilized.The level of satisfaction across the various types of sensors ranged from a mean low of 4 for medication sensors (i.e., somewhat satisfied) with the rest scoring at least a mean of 4.5, indicating between somewhat satisfied and satisfied.The participants indicated a high level of utility of the technology, ranging from 3.4 to 5.5 with 6 indicating strongly agree.Conclusion: The results of this research contribute to our scant knowledge about the high level of satisfaction and utility of passive RMT from those with real-life experience using this technology.Mechanisms should be investigated to support the implementation of passive RMT for caregivers of frail older adults.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".