Perspectives of GPs working in or alongside emergency departments in England: qualitative findings from the GPs and Emergency Departments Study
Bibliographic record
Abstract
BACKGROUND: Around 43% of emergency department (ED) attendances can be managed in general practice. Strategies to address this include directing appropriate patients to GPs working in or alongside EDs (GPED). Views of GPs choosing to work in GPED roles may inform planning and implementation of GPED services as well as wider general practice provision. AIM: To explore the experiences and motivations of GPs choosing to work in GPED services in England, and to identify factors that may support or hinder GPs working in GPED roles. DESIGN AND SETTING: Thematic analysis of 42 semi-structured interviews of GPs working in 10 GPED case sites across England. METHOD: Qualitative GP interviews from a mixed-methods study of GPs in GPED roles were thematically analysed in relation to research aims. RESULTS: Four themes were generated: the 'pull' of a portfolio career; the 'push' of disillusionment with general practice; professional reciprocity; sustainability of GPED services and core general practice. Flexible, favourable working conditions, collaboration, and professional development made GPED an attractive workplace, often as part of a portfolio career or after retiring from core general practice. Working in GPED services was largely driven by disillusionment with core general practice. Both GPED and core general practice were thought to benefit from GPED GPs' skills. There were concerns about GPED sustainability and destabilisation of core general practice. CONCLUSION: GPED may extend the clinical careers of experienced GPs and support recruitment and retention of more recently qualified GPs. Despite some benefits, GPED may destabilise core general practice and increase pressure on both environments.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".