Comparing the content and quality of video, telephone, and face-to-face consultations: a non-randomised, quasi-experimental, exploratory study in UK primary care
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.
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".