Training needs for staff providing remote services in general practice: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Contemporary general practice includes many kinds of remote encounter. The rise in telephone, video and online modalities for triage and clinical care requires clinicians and support staff to be trained, both individually and as teams, but evidence-based competencies have not previously been produced for general practice. AIM: To identify training needs, core competencies, and learning methods for staff providing remote encounters. DESIGN AND SETTING: Mixed-methods study in UK general practice. METHOD: Data were collated from longitudinal ethnographic case studies of 12 general practices; a multi-stakeholder workshop; interviews with policymakers, training providers, and trainees; published research; and grey literature (such as training materials and surveys). Data were coded thematically and analysed using theories of individual and team learning. RESULTS: Learning to provide remote services occurred in the context of high workload, understaffing, and complex workflows. Low confidence and perceived unmet training needs were common. Training priorities for novice clinicians included basic technological skills, triage, ethics (for privacy and consent), and communication and clinical skills. Established clinicians' training priorities include advanced communication skills (for example, maintaining rapport and attentiveness), working within the limits of technologies, making complex judgements, coordinating multi-professional care in a distributed environment, and training others. Much existing training is didactic and technology focused. While basic knowledge was often gained using such methods, the ability and confidence to make complex judgements were usually acquired through experience, informal discussions, and on-the-job methods such as shadowing. Whole-team training was valued but rarely available. A draft set of competencies is offered based on the findings. CONCLUSION: The knowledge needed to deliver high-quality remote encounters to diverse patient groups is complex, collective, and organisationally embedded. The vital role of non-didactic training, for example, joint clinical sessions, case-based discussions, and in-person, whole-team, on-the-job training, needs to be recognised.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".