Comparing the content and quality of video, telephone, and face-to-face consultations: a non-randomised, quasi-experimental, exploratory study in UK primary care
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Growing demands on primary care services have led to policymakers promoting video consultations (VCs) to replace routine face-to-face consultations (FTFCs) in general practice. AIM: To explore the content, quality, and patient experience of VC, telephone (TC), and FTFCs in general practice. DESIGN AND SETTING: Comparison of audio-recordings of follow-up consultations in UK primary care. METHOD: Primary care clinicians were provided with video-consulting equipment. Participating patients required a smartphone, tablet, or computer with camera. Clinicians invited patients requiring a follow-up consultation to choose a VC, TC, or FTFC. Consultations were audio-recorded and analysed for content and quality. Participant experience was explored in post-consultation questionnaires. Case notes were reviewed for NHS resource use. RESULTS: Of the recordings, 149/163 were suitable for analysis. VC recruits were younger, and more experienced in communicating online. FTFCs were longer than VCs (mean difference +3.7 minutes, 95% confidence interval [CI] = 2.1 to 5.2) or TCs (+4.1 minutes, 95% CI = 2.6 to 5.5). On average, patients raised fewer problems in VCs (mean 1.5, standard deviation [SD] 0.8) compared with FTFCs (mean 2.1, SD 1.1) and demonstrated fewer instances of information giving by clinicians and patients. FTFCs scored higher than VCs and TCs on consultation-quality items. CONCLUSION: VC may be suitable for simple problems not requiring physical examination. VC, in terms of consultation length, content, and quality, appeared similar to TC. Both approaches appeared less 'information rich' than FTFC. Technical problems were common and, though patients really liked VC, infrastructure issues would need to be addressed before the technology and approach can be mainstreamed in primary care.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it