Mobile Usability Testing in Healthcare: Methodological Approaches.
Bibliographic record
Abstract
The use of mobile devices and healthcare applications is increasing exponentially worldwide. This has lead to the need for the healthcare industry to develop a better understanding of the impact of the usability of mobile software and hardware upon consumer and health professional adoption and use of these technologies. There are many methodological approaches that can be employed in conducting usability evaluation of mobile technologies. More obtrusive approaches to collecting study data may lead to changes in study participant behaviour, leading to study results that are less consistent with how the technologies will be used in the real-world. Alternatively, less obstrusive methods used in evaluating the usability of mobile software and hardware in-situ and laboratory settings can lead to less detailed information being collected about how an individual interacts with both the software and hardware. In this paper we review and discuss several innovative mobile usability evaluation methods on a contiuum from least to most obtrusive and their effects on the quality of the usability data collected. The strengths and limitations of methods are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.228 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".