The availability of general practice in Ireland's Mid-West Region: does the 'Inverse Care Law' still apply?
Bibliographic record
Abstract
INTRODUCTION: The 'Inverse Care Law' suggests the availability of good medical care tends to vary inversely with the needs of the local population. Dr Julian Tudor Hart's observations related to lack of access to care for those in both socially deprived and geographically remote areas. In this study, we aim to examine if the 'Inverse Care Law' is still relevant to GP service provision in the Mid-West of Ireland. METHODS: GP clinic locations in Limerick and Clare were identified using the Health Service Executive (HSE) Service Finder and geocoded. GeoHive.ie was used to determine Electoral District (ED) centroids across the Mid-West. The shortest linear distance to a GP clinic was calculated for each ED. PobalMaps.ie was used to determine population and social deprivation scores of each ED. RESULTS: In total, 122 GP practices were identified across 324 EDs. The average travel distance to a GP clinic in the Mid-West is 4.7 km. Limerick City EDs had the smallest patient population per GP clinic and were all found to be within 1.5 km of a GP clinic. Proximity to GP clinics did not correlate with deprivation. However, by removing GP clinics from the analyses, it was possible to determine how vulnerable different areas (rural vs urban, deprived vs affluent) are to potential changes in GP clinic availability in the future. DISCUSSION: People living in urban areas such a Limerick City have improved geographic accessibility to GP clinics compared with their rural counterparts. However, within urban areas assessed, GP clinics were rarely found in deprived areas. Therefore, remote and urban-deprived areas are far more vulnerable to negative proximity effects secondary to practice closures, suggesting the principles of the 'Inverse Care Law' may still be active in the Mid-West of 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 | 0.018 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".