Exploring usability characteristics in computer-based digital health technologies for family caregivers of people with chronic progressive conditions: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to map the literature on how usability is considered during the design and/or evaluation of computer-based digital health technologies for family caregivers of persons with chronic progressive conditions. INTRODUCTION: Computer-based digital health technologies offer convenient alternatives for delivering interventions to caregivers of people with chronic progressive conditions. Usability is a critical component of good practice in developing and implementing health and social care technologies; however, we need to determine whether usability is incorporated in the design and/or evaluation of computer-based digital health technologies for caregivers of people with chronic progressive conditions. Within this context, a broad overview of the existing literature on usability in computer-based digital health technologies is needed. INCLUSION CRITERIA: We will include studies published from 2012 to the present that describe usability characteristics of computer-based digital health technologies targeting adult (≥18 years old) family caregivers of people with chronic progressive conditions, regardless of study design or setting. METHODS: We will use the JBI methodology for scoping reviews. We will conduct searches of MEDLINE (Ovid), PsycINFO (Ovid), CINAHL (EBSCOhost), and Web of Science Core Collection to capture eligible studies. After the results are deduplicated, 2 independent reviewers will assess each study for eligibility and extract data from the included studies. Conflicts will be resolved through discussion or with a third reviewer. Data analysis will use a textual narrative synthesis approach. REVIEW REGISTRATION: Open Science Framework osf.io/w4vk5.
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 | 0.131 | 0.119 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.022 | 0.014 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".