The acceptability to patients of video-consulting in general practice: semi-structured interviews in three diverse general practices.
Bibliographic record
Abstract
BACKGROUND: To improve patient access to healthcare, the UK government has encouraged technology-based approaches including internet video-consulting. However, little is known about patient acceptance of video-consulting as a consulting method. We aimed to explore primary care patients' views video-consulting. METHOD: We used semi-structured interviews to survey 270 patients in NHS Lothian. Three diverse General Practices were chosen purposively and sequential patients attending the practice at a range of different times of day were invited to participate. Patients were asked to indicate their level of computer proficiency and provide their views on the use of video-call consulting and what specific applications it might have. We found that 135 of 270 respondents (50%, 95% CI 43.9%-56.1%) would use video-consulting. Patients under 60 years were over two times more likely to use it (OR 2.2, 95% CI 2.1-6.6, n = 248) and evidence of a positive trend between increasing computer proficiency and those who would video-consult was found, (χ2 = 43.97, p < 0.0005, n=270). Patients who had previously used video-calling services (such as Skype™)were approximately six times more likely to favour video-consulting than those who had not (OR 5.9, 95% CI 3.5-9.9, n = 270). CONCLUSIONS: This suggests strong patient interest in video-consulting in primary care, however, it is possible that in the short to medium term there may be access inequality favouring younger and more technically able people. Further studies are needed to determine the content, safety, efficacy and cost-effectiveness of employing this medium.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".