Establishing a Deep End GP group: a scoping review
Bibliographic record
Abstract
BACKGROUND: GPs working in deprived areas, where all-cause mortality rates are higher compared to less deprived areas, face unique challenges. Despite 50 years passing since Tudor Hart's seminal 'inverse care law' paper, the health inequities gap remains wide. Deep End GP groups are frontline GP-led initiatives working to close this gap and improve the health and lives of those most in need. AIM: To use scoping methodology to map out the process of creating a Deep End GP group. DESIGN & SETTING: A scoping review using Arksey and O'Malley's framework. METHOD: MEDLINE, Embase, Web of Science, and CINAHL databases, as well as non-peer reviewed publications, were searched and articles extracted, reviewed, and analysed according to iterative inclusion criteria. RESULTS: From an initial search number of 35 articles, 16 articles were included in the final analysis. Key steps in starting a Deep End GP group were: quantifying patients and practices in areas of deprivation; establishing GP-led objectives at an initial meeting; regular steering group meetings with close collaboration between academic and frontline general practice, as well as the wider multidisciplinary team; and adopting a local Deep End logo. CONCLUSION: Deep End GP groups have made advances to reduce health impacts of systemic health inequities. Starting a Deep End GP group involves a multidisciplinary approach, beginning with the identification of patients and practices in areas of highest need. The findings and key themes identified in this scoping review will guide interested parties to start the journey to do the same in their locality and to join the Deep End movement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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 teacher head, 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".