Identifying Facilitators of and Barriers to Digital Health Literacy in Pediatric Rheumatic Diseases: A Scoping Review Protocol
Bibliographic record
Abstract
Abstract Background Digital health literacy (DHL) is a set of skills needed to positively integrate health information acquired from digital sources into health behaviors and lifestyles. Digital health is defined as the use of technology to deliver, manage, discuss, and improve health. Higher levels of DHL are associated with better health-related outcomes and disease management, while lower levels of DHL serve as a barrier to improving health. In the context of childhood rheumatic diseases, fostering robust DHL can empower children, adolescents, and their primary caregivers to navigate the complexities of managing and understanding health information in this digital era. The objective of this report is to develop a protocol that can be used to identify existing assessments of DHL and knowledge about factors that promote or impede DHL in children and adolescents with chronic rheumatic diseases and their primary caregivers. Methods The scoping review protocol applies the JBI scoping review framework and the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for scoping reviews. The protocol will identify full-text English language studies and dissertations published after 1974 that identify facilitators of and barriers to DHL in patients younger than age 19-years diagnosed with a chronic rheumatic disease and/or their primary caregivers. In this protocol qualitative, quantitative, and mixed methods studies are eligible for inclusion with no limitation on the geographical origins of the publications; grey literature will be excluded. The protocol requires two researchers to independently screen titles and abstracts for inclusion, followed by a full text review for data extraction. Descriptive statistics around identified facilitators of and barriers to DHL, populations studied, and other pertinent data will be provided. Qualitative data will be open coded and categorized around the research questions. A narrative summary will accompany the results. Discussion The novel and comprehensive search strategy outlined by this protocol will enable ongoing research related to the interaction of technology, the healthcare system, and individuals affected by having a pediatric rheumatic disease. Scoping review registration The protocol has been preregistered on the Open Science Framework ( https://osf.io/g4jbh/ ).
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 | 0.130 | 0.122 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.086 | 0.016 |
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".