Revolution in UK General Practice Due to COVID-19 Pandemic: A Cross-Sectional Survey
Bibliographic record
Abstract
Objectives To assess how UK General Practitioners (GPs) and Practice Managers (PMs) have coped with the challenges posed by the coronavirus disease-19 (COVID-19) pandemic and whether they felt adequately supported by the wider National Health Service (NHS). Methods This is a cross-sectional survey. All GPs and PMs (total 1,354) in Leicester, Leicestershire, and Rutland (LLR) were invited to participate in an online questionnaire. Results A total of 95 invitees completed the survey. Over a quarter had required time off work due to COVID symptoms or contact. All respondents described either introducing or increasing the use of remote patient consultations. Most striking was the rise in video consultations from just 3% to 95% during the pandemic. Almost half of the feedback on the usefulness of remote consultations were positive, 16% were negative and 17% were mixed. The most commonly cited benefit was time efficiency. Drawbacks of remote consultations included technical difficulties and poor patient communication. Practice premises, systems and processes also required significant modifications during the pandemic to ensure the provision of safe clinical care, including reception screens, one-way patient flow, greater infection prevention measures. However, despite their ability to introduce such widescale change virtually overnight, over 10% of respondents reported that the strain had placed their practice at risk of closure. Over half of respondents felt they were not provided with adequate personal protective equipment (PPE) for the safety of their staff. Perception of the support provided by NHS England and the Clinical Commissioning Groups (CCGs) was rather mixed, although additional guidelines were broadly welcomed. The most requested enduring changes related to remote patient consultations (59%) and remote triage (19%). However, in order to support such largescale permanent change, study respondents felt that a different funding and financial structure is required together with improved IT infrastructure, greater patient education and a more supportive regulatory environment. Conclusions COVID-19 has substantially accelerated the pace of change within NHS primary care. The long-term fear is that there may be insufficient financial and clinical backing from regulatory bodies to support such rapid and far-reaching changes.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".