Usability Methods and Attributes Reported in Usability Studies of Mobile Apps for Health Care Education: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: E-learning technologies, including mobile apps, are used to a large extent in health care education. Mobile apps can provide extendable learning environments and motivate students for adaptive and collaborative learning outside the classroom context. Developers should design practical, effective, and easy-to-use mobile apps. Usability testing is an important part of app development in order to understand if apps meet the needs of users. OBJECTIVE: The aim of this study is to perform a scoping review of usability methods and attributes reported in usability studies of mobile apps for health care education. METHODS: The scoping review is guided by the methodological framework developed by Arksey & O'Malley and further developed by Levac et al and Kahlil et al. The stages we will follow are as follows: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies; (4) charting the data; and (5) summarizing and reporting the results. We have developed two research questions to meet the aim of the study, which are as follows: (1) What usability methods are used to evaluate the usability of mobile apps for health care education? and (2) What usability attributes are reported in the usability studies of mobile apps for health care education? We will apply a comprehensive search of the literature, including 10 databases, a reference search, and a search for grey literature. Two review authors will independently screen articles for eligibility. RESULTS: The initial electronic database searches were completed in March 2019. The literature search identified 14,297 unique references. Following title and abstract screening, the full texts of 369 records were obtained. The scoping review is expected to be completed in spring 2021. CONCLUSIONS: We expect the overview of usability methods and attributes reported in usability studies of mobile apps for health care education to contribute to the knowledge base for researchers and developers. It will give an overview of the research field and provide researchers and developers with relevant and important information on the usability research area, including highlighting possible research gaps. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19072.
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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 | 0.155 | 0.170 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.046 | 0.013 |
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".