Digital Health and Digital Learning Experiences Across Speech-Language Pathology, Phoniatrics, and Otolaryngology: Interdisciplinary Survey Study
Bibliographic record
Abstract
BACKGROUND: Advances in digital health and digital learning are transforming the lives of patients, health care providers, and health professional students. In the interdisciplinary field of communication sciences and disorders (CSD), digital uptake and incorporation of digital topics and technologies into clinical training programs has lagged behind other medical fields. There is a need to understand professional and student experiences, opinions, and needs regarding digital health and learning topics so that effective strategies for implementation can be optimized. OBJECTIVE: This cross-sectional survey study aims to interdisciplinarily investigate professional and student knowledge, use, attitudes, and preferences toward digital health and learning in the German-speaking population. METHODS: An open-ended, web-based survey was developed and conducted with professionals and students in CSD including phoniatricians and otolaryngologists, speech-language pathologists (German: Logopäd*innen), medical students, and speech-language pathology students. Differences in knowledge, use, attitudes, and preferences across profession, generation, and years of experience were analyzed. RESULTS: A total of 170 participants completed the survey. Respondents demonstrated greater familiarity with digital learning as opposed to eHealth concepts. Significant differences were noted across profession (P<.001), generation (P=.001), and years of experience (P<.001), which demonstrated that students and younger participants were less familiar with digital health terminology. Professional (P<.001) and generational differences were also found (P=.04) in knowledge of digital therapy tools, though no significant differences were found for digital learning tools. Participants primarily used computers, tablets, and mobile phones; non-eHealth-specific tools (eg, word processing and videoconferencing applications); and digital formats such as videos, web courses, and apps. Many indicated a desire for more interactive platforms, such as virtual reality. Significant differences were found across generations for positive views toward digitalization (P<.001) and across profession for feelings of preparedness (P=.04). Interestingly, across profession (P=.03), generation (P=.006), and years of experience (P=.01), students and younger participants demonstrated greater support for medical certification. Commonly reported areas of concern included technical difficulties, quality and validity of digital materials, data privacy, and social presence. Respondents tended to prefer blended learning, a limited to moderate level of interactivity, and time and space-flexible learning environments (63/170, 37.1%), with a notable proportion still preferring traditional time and space-dependent learning (49/170, 28.8%). CONCLUSIONS: This comprehensive investigation into the current state of CSD student and professional opinions and experiences has shown that incorporation of digital topics and skills into academic and professional development curricula will be crucial for ensuring that the field is prepared for the ever-digitalizing health care environment. Deeper empirical investigation into efficacy and acceptance of digital learning and practice strategies and systematic training and practical organizational supports must be planned to ensure adaptive education and practice.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".