Design and evaluation of web interfaces for informal care providers in senior monitoring
Bibliographic record
Abstract
Abstract It has been recognized that most seniors prefer to age in a place with familiar surroundings until their health makes this impossible. In an attempt to address the aging phenomenon, as well as recognize seniors' preference, we worked with a Canada‐based company that develops a sensor‐based home monitoring system for people to monitor the home activities of independently living seniors. Our role was to develop web interfaces that present sensor data to the intended web users – the seniors' informal care providers (e.g., their close friends or family members). In this paper, we present the information design and the web interface prototypes, and report the results of our formative evaluations through cognitive walkthrough and heuristic evaluation methods. The common problems discovered in both methods were problematic notification mechanism, inconsistency, background and layout. Each method also detected usability issues that the other did not. Our work adds more empirical evidence to the importance of combining evaluation methods in a study. The experiences in this study also helped us reflect on approaches and strategies when working with industry partners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".