Interventions, Participative Role, Barriers, and Facilitators for Involvement in eHealth Communication for People Undergoing Hemodialysis: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: eHealth interventions have been shown to offer people living with chronic kidney disease the opportunity of embracing dialysis therapies with greater confidence, the potential to obtain better clinical outcomes and increased quality of life, and diverse and flexible designs and delivery options. eHealth interventions or solutions can offer one-way information without the possibility for dialogue, as with most mobile apps. eHealth interventions intending to enable two-way communication between patients undergoing hemodialysis and health professionals are the focus of this review. eHealth communication interventions that enable two-way communication between patients undergoing hemodialysis and health professionals is an emerging field, but issues relating to participation in eHealth communication for patients undergoing hemodialysis are scarcely described. The current conceptualization of this issue is too scattered to inform the development of future interventions. In this scoping review, we want to assemble and examine this scattered knowledge on participation in two-way eHealth communication for patients undergoing hemodialysis. OBJECTIVE: We want to understand the participative role of people living with chronic kidney disease undergoing hemodialysis in available communicative eHealth interventions and to understand which barriers and facilitators exist for patient involvement in eHealth communication with health professionals. METHODS: A scoping review methodology is guiding this study. Peer-reviewed primary studies, including quantitative, qualitative, and mixed methods study designs will be included. A systematic search for published studies, dissertations, and theses at the doctoral level in the English language will be conducted in five databases (MEDLINE, Embase, CINAHL, Scopus, and ProQuest Dissertations and Theses). The included literature will focus on adult (18 years or older) patients undergoing hemodialysis who are involved in eHealth communication with health professionals. Data on the type of eHealth communication interventions, the participative role, and barriers and facilitators for the involvement in eHealth communication for people undergoing hemodialysis will be extracted independently by two reviewers. The extracted data will be collected in a draft charting table prepared for the study. Any discrepancies between the reviewers will be solved through discussion or with a third reviewer. RESULTS: Results are anticipated by the spring of 2023 and will be presented in tabular format along with a narrative summary. The anticipated results will be presented in alignment with the objectives of the study, presenting findings on the participative role of patients undergoing hemodialysis in eHealth communication interventions. CONCLUSIONS: We anticipate that this study will inform on eHealth communication interventions and the level of patient participation in eHealth communication for patients undergoing hemodialysis. The systematized overview will possibly identify research gaps and motivate further development of eHealth communication to ensure patient participation. The findings will be of interest to key stakeholders in clinical care, research, development, policy, and patient advocacy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/38615.
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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 | 0.087 | 0.074 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.068 | 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".