The doctor-patient relationship and barriers in non-verbal communication during teleconsultation in the era of COVID-19: A scoping review
Bibliographic record
Abstract
Background: Telemedicine is increasingly being used to provide virtual medical care. However, the transition to virtual consultations presents challenges for non-verbal communication. This scoping review aimed to identify and summarize studies that present data on barriers to non-verbal communication during teleconsultation. Methods: We searched MEDLINE/Pubmed, Ovid, APA, EBSCO, Web of Science and Scielo, without language or region restrictions. Our study included case series, cross-sectional, retrospective, and prospective cohorts that addressed barriers in any aspect of the non-verbal communication during teleconsultation. The quality of the evidence was assessed by the New Castle-Ottawa and Murad tools, and a thematic analysis was used for the qualitative synthesis of results. Results: We included 18 studies that reported qualitative findings related to the dimensions of non-verbal communication in telemedicine, which include ‘head and face’, ‘voice and speech’, ‘body language’, and ‘technical aspects’. The most reported barriers were facial gestures, looks, and body posture. Conclusions: Our study identified several dimensions of non-verbal communication that may pose barriers during teleconsultation. These findings may help guide the development of strategies to address these barriers and improve the quality of telemedicine services.
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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 | 0.025 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".