Descriptive Review of Online Information Resources for People With Stroke: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: People with stroke and their caregivers experience numerous information needs; internet-based resources may offer cost-effective ways to improve access to information about this condition and its management, including the availability of resources and support. The quality of online health information is, therefore, an important consideration for both developers and consumers of these online resources. OBJECTIVE: This study aims to map and evaluate the content, readability, understandability, design, and quality characteristics of freely available online information resources (ie, websites) that empower people with stroke and their caregivers with information and self-help strategies poststroke. METHODS: This descriptive review will follow the five systematic and rigorous methodological steps that are recommended for scoping reviews, which include the following: (1) identifying the research question, (2) identifying relevant studies, (3) selecting the studies, (4) charting the data, and (5) collating, summarizing, and reporting the results. Data will then be synthesized and analyzed thematically. RESULTS: As of February 2021, the scoping review is in the data extraction stage. Data will be synthesized, and the first results are expected to be submitted for publication in an open-access peer-reviewed journal in August 2021. In addition, we will develop an accessible summary of the results for stakeholder meetings. Ethical approval is not required for this review, as it will only include publicly available information. CONCLUSIONS: This study is novel and will evaluate the typology, content, and design-related criteria, including accessibility, aesthetics, navigability, interactivity, privacy, and data protection, of online information resources for stroke. The review will be limited to online resources published in English. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/23174.
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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 | 0.116 | 0.129 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.078 | 0.015 |
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".