Telemedicine and doctor-patient communication: an analytical survey of the literature
Bibliographic record
Abstract
The literature about the effect of telemedicine on doctor-patient communication was reviewed. A total of 38 studies were identified: six were surveys of provider and community attitudes; 21 were post-encounter surveys of participants in a medical consultation; and 11 were qualitative analyses of behaviour in a medical encounter. Twenty-one of the 38 investigations originated in the USA, six in the UK, four in Australia, three in Norway, two in Canada, one in Finland and one in Sweden. All were relatively recent. The findings from each study were coded according to 23 categories developed from the literature and a positive or negative rating was assigned to each of the 213 communication results. Approximately 80% of abstracted findings favoured telemedicine, with all but two of the 23 categories analysed (non-verbal behaviour and lack of touch) reporting more positive than negative results. Verbal content analysis is important for the development of interventions aimed at facilitating doctor-patient telecommunication. However, further research is necessary if the nature and content of the communication process are to be fully understood.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".