The doctor-patient relationship and barriers in non-verbal communication during teleconsultation in the era of COVID-19: A scoping review
Bibliographic record
Abstract
<ns3:p> <ns3:bold>Background:</ns3:bold> 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. </ns3:p> <ns3:p> <ns3:bold>Methods:</ns3:bold> 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. </ns3:p> <ns3:p> <ns3:bold>Results:</ns3:bold> 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. </ns3:p> <ns3:p> <ns3:bold>Conclusions:</ns3:bold> 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. </ns3:p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".