Pediatric Clinicians’ Use of Telemedicine: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Bedside manner describes how clinicians relate to patients in person. Telemedicine allows clinicians to connect virtually with patients using digital tools. Effective virtual communication or webside manner may require modifications to traditional bedside manner. OBJECTIVE: This study aims to understand the experiences of telemedicine providers with patient-to-provider virtual visits and communication with families at a single large-volume children's hospital to inform program development and training for future clinicians. METHODS: A total of 2 focus groups of pediatric clinicians (N=11) performing virtual visits before the COVID-19 pandemic, with a range of experiences and specialties, were engaged to discuss experiential, implementation, and practice-related issues. Focus groups were facilitated using a semistructured guide covering general experience, preparedness, rapport strategies, and suggestions. Sessions were digitally recorded, and the corresponding transcripts were reviewed for data analysis. The transcripts were coded based on the identified main themes and subthemes. On the basis of a higher-level analysis of these codes, the study authors generated a final set of key themes to describe the collected data. RESULTS: Theme consistency was identified across diverse participants, although individual clinician experiences were influenced by their specialties and practices. A total of 3 key themes emerged regarding the development of best practices, barriers to scalability, and establishing patient rapport. Issues and concerns related to privacy were salient across all themes. Clinicians felt that telemedicine required new skills for patient interaction, and not all were comfortable with their training. CONCLUSIONS: Telemedicine provides benefits as well as challenges to health care delivery. In interprofessional focus groups, pediatric clinicians emphasized the importance of considering safety and privacy to promote rapport and webside manner when conducting virtual visits. The inclusion of webside manner instructions within training curricula is crucial as telemedicine becomes an established modality for providing health care.
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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".