Experiences of GP trainees in undertaking telephone consultations: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Primary care telephone consultations are increasingly used for patient triage, reviews, and providing clinical information. They are also a key postgraduate training component yet little is known about GP trainees' preparation for, or experiences and perceptions of, them. AIM: To understand the experiences, perceptions, and training of GP trainees in conducting telephone consultations. DESIGN & SETTING: A mixed-methods study was undertaken of North Central and East London (NCEL) GP trainees. METHOD: A cross-sectional electronic survey of trainees was performed with subsequent semi-structured interviews. Survey data were analysed using descriptive statistics, and qualitative data using thematic analysis. RESULTS: <0.0001). Positive experiences included managing workload and convenience. Negative experiences included complex encounters, communication barriers, and absence of examination. Trainees reported that training for telephone consultations needed strengthening, and that recently introduced audio-clinical observation tools (COTs) were useful. Positive correlations were found between the length of out-of-hours (OOH) but not in-hours training and the level of supervision or feedback received for telephone consultations. CONCLUSION: This project sheds light on GP trainees' current experiences of telephone consultations and the need to enhance future training. The findings will inform a wider debate among stakeholders and postgraduate learners regarding training for telephone consultations, and potentially for other remote technologies.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".