Do undergraduate general practice placements propagate the ‘inverse care law’?
Bibliographic record
Abstract
INTRODUCTION: Fifty years since Dr Tudor-Hart's publication of the 'Inverse Care Law', all-cause mortality rates and COVID-19 mortality rates are higher in more deprived areas. Part of the solution is to increase access and availability to healthcare in underserved and deprived areas. This paper examined how socio-economically representative the undergraduate general practice placements are in Northern Ireland (NI). METHODS: A quantitative study of general practices involved in undergraduate medical placements through Queen's University Belfast, comparing practice lists by deprivation indices, examining both blanket deprivation and deprivation quintile trends for teaching and non-teaching practices. RESULTS: Deprivation data for 135 teaching practices were compared against the 323 NI practices. Teaching practices had fewer patients living in the most deprived quintiles compared with non-teaching practices. Fewer practices with blanket deprivation were involved in undergraduate medical education, 32% compared with 42% without blanket deprivation. Practices in areas of blanket deprivation were under-represented as teaching practices, 10%, compared to 14% of NI general practices that met this criterion. CONCLUSION: Practices with blanket deprivation were under-represented as teaching practices. Exposure to general practice in deprived areas is an essential step to improving future workforce recruitment and ultimately to closing the health inequalities gap. Ensuring practices in high-need areas are proportionately represented in undergraduate placements is one way to direct action in addressing the 'Inverse Care Law'. This study is limited to NI and further work is required to compare institutions across the UK and Ireland.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".