Editorial: Integrating digital health technologies in clinical practice and everyday life: unfolding innovative communication practices
Bibliographic record
Abstract
These technologies also provide access to tailored educational resources and enhanced health communication strategies. At the same time, their use presents complex social, organizational, communicational, and interactional challenges. Such challenges include how to build constructive relationships with and through technology and how to improve health communication to engage people in self-care practices or limit possible physical, psychological, or emotional harms for patients. Broadly speaking, the integration of digital health technologies into clinical practice and the daily lives of patients thus remains a major challenge for health organizations.The eight articles featured in this special issue focus on various communication practices related to the use of digital health technologies by patients and healthcare providers. Three articles focus on the transformations of patient-provider communication and relationships during technology-enabled consultations and treatment. Using a multi-modal conversation analysis approach, Dalmeijer and colleagues examine the role of digital technology in interactions between occupational therapists (OTs) and parents of infants and toddlers with cerebral palsy taking part in a pediatric rehabilitation program. Stumpël and colleagues conducted a qualitative interview study to explore the perspectives of health care professionals in neonatal intensive care units on the impact of webcams on communication with parents and family-centred care. Branley et al.'s experimental study examines patients' preferences for consultations with physicians or chatbots when seeking advice for embarrassing and stigmatizing conditions. Three articles address new forms of interactions between health care professionals enabled by technology. Trupia and colleague's qualitative interview study describes the various uses of tele-expertise in dermatology and explores the dermatologists' perspectives on virtual interactions with their colleagues to produce a diagnostic opinion at a distance. Weiste et al. use conversation analysis to study how professionals involved in return-to work negotiations use meeting memos to facilitate opportunities for participation during virtual meetings. Mlynár and colleagues' ethnomethodological/conversation analysis study reports on interactions between physicians and medical radiology technicians when they were learning to use an artificial intelligence medical imaging platform.Finally, two articles raise issues related to the acceptability of digital health technologies and explore solutions to support their implementation and use. Gauthier-Beaupré and Grosjean present a meta-ethnographic review on the social acceptability of digital health technologies by French-speaking minority communities in Canada. Davat and colleague's study explores the aspirations and challenges encountered by health care providers, patients, technology designers, and researchers when employing participatory design methodologies to develop monitoring devices for heart failure.We would like to thank all the authors for their contributions to this special issue. Thank you also to all the reviewers who have supported the peer review process.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".