General Practitioners at the Deep End: The experience and views of general practitioners working in the most severely deprived areas of Scotland.
Bibliographic record
Abstract
This Occasional Paper captures the experience and views of general practitioners (GPs) at the Deep End, comprising GPs serving the 100 most deprived practice populations in Scotland. The paper builds on a series of articles that were published in the British Journal of General Practice in 2011. Deep End practices are at the front line of the NHS in addressing the health and healthcare problems of severely deprived communities. Although they have substantial knowledge, experience and authority, they have largely been neglected in discussions, reports and policies about inequalities in health. By increasing the volume, quality and consistency of care provided for individual patients, and harnessing the intrinsic strengths of general practice – including coverage, continuity, coordination, flexibility, long-term relationships and trust – general practices in very deprived areas can improve population health and narrow inequalities. There are two important barriers. The first is the inverse care law, best understood as the result of NHS policies that restrict access to care based on need, and which is manifest every day as the shortage of time within consultations to address patients’ needs. Second, there are dysfunctional links with the wide range of other professions and services, whose contributions and partnership are needed to deliver needs-based care. There is no single or short-term solution. A sustained and integrated package of measures is needed, combining at least six key elements. Deep End practices need more time and capacity to address unmet need. Best use needs to be made of serial encounters over long periods. Practices need to be better connected with other professions and services as hubs of local health systems. There need to be better connections between practices across the front line, following the example of the Deep End Project. The front line needs to be better informed and supported by NHS organisations. Leadership needs to be developed and supported at practice and area level for all of these activities. The NHS should be at its best where it is needed most. Otherwise, the delivery of effective health care will widen rather than narrow health inequalities. Deep End practices can lead the way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".