INTERNET-BASED INTERVENTIONS FOR CAREGIVERS OF ASSISTIVE TECHNOLOGY USERS: NEEDS AND PERCEPTIONS
Bibliographic record
Abstract
Providing home-based care to older adults using assistive technology (AT) (e.g. mobility aids, communication aids) can be challenging for family caregivers. MOvIT-PLUS™ is an Internet-based intervention aiming to offer remote monitoring, support and training to dyads of family caregivers and older adults using AT in their daily lives. Using an iterative user-centred design approach, 30 semi-structured interviews were conducted with end-users and key informants to i) identify end-user needs through discussion about past experiences with AT, and ii) explore end-users’ perceptions of a mock-up of MOvIT-PLUS™. A modified content analysis approach was used to identify themes from a mix of emerging and expected concepts. Results indicate AT procurement is viewed as an ongoing cyclical process, with potential unmet needs at key moments before and after AT procurement. When expressing their preferences about the MOvIT-PLUS™ mock-up, end-users and key informants were generally supportive of automated monitoring calls and asynchronous training features, such as skill-based video bank. Moreover, end-users express their appreciation regarding professional-led counselling and training features such as videoconferences, but key informants had divergent opinions. These results are guiding the MOvIT-PLUS™ prototype design towards a graded support approach, starting with empowering end-users to resolve AT-related challenges and then adding professional support when needed. This study highlights that Internet-based interventions dedicated to family caregivers should consider concrete daily task challenges, ensure adequate follow-up and offer human support.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".