Impact of Training and Education Programs for Health Care Professionals on Video and Text-Based Meetings in Ensuring Health Care Quality: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: The use of video meetings and text-based meetings has surged and emerged as a critical tool in health care. These tools offer many benefits, such as patient prescreening, counseling services, remote patient tracking, and monitoring. With the increasing demand for technologies, health care professionals require training and educational competency development to sustain in the modern digital age. This necessitates synthesizing evidence about the existing training programs in arranging and regulating such meetings, the implementation, and reassurance about the effectiveness of these digital health meetings. OBJECTIVE: The synthesis will also uncover what training programs for health care professionals to conduct video and text-based meetings are available, and if so, how they were implemented and their impacts from the perspectives of the organization, the staff, and the patients. METHODS: The review will follow the Joanna Briggs Institute (JBI) methodology. The published studies will be searched in APA PsycInfo, PubMed, and CINAHL, and the unpublished studies through Mednar, Trove, OCLC WorldCat, Dissertations, and Theses. Studies published in English from 2003 will be considered. This review will include studies of health care professionals trained to communicate online with patients or service users, health care professionals, and health care organizations. The concept will involve online communication, such as conducting video and text-based meetings (emails, chats, and web portals), and the context will consider studies based on health care, hospitals or clinics, and primary care. A broad scope of evidence, including quantitative, qualitative, text, and opinion studies, will be considered. A total of 2 independent reviewers will screen the titles and abstracts and review the full text. Data will be extracted from the included studies using a data extraction tool developed for this study. RESULTS: The results will be presented in a PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flow diagram. A draft charting table will be developed as a data extraction tool. The results will be presented as a "map" of the data in a logical, diagrammatic, or tabular form and a descriptive format. This protocol was first developed by the principal author at Linnaeus University in April 2022; however, a full search was undertaken in August 2024 as part of research development at the University of Bradford. CONCLUSIONS: The review will identify the knowledge gaps, clarify the concepts, examine emerging evidence, and thus make recommendations for future research on video consultation and text-based meetings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69963.
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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 | 0.139 | 0.132 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.023 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 0.009 |
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".