Are GP training opportunities in Northern Ireland widening or closing the gap on health inequalities? An analysis of Northern Ireland deprivation data
Bibliographic record
Abstract
BACKGROUND: Increasing the GP workforce will not necessarily level up healthcare provision. Instead, increasing GP training numbers could worsen health inequity and inequalities. This is especially true if there are fewer opportunities to learn, train, and build confidence in underserved, socioeconomically deprived areas. AIM: To investigate the representation of socioeconomic deprivation in postgraduate GP training practices in Northern Ireland (NI). DESIGN & SETTING: An analysis of socioeconomic deprivation indices and scores of GP practices in NI involved in postgraudate GP training. METHOD: The socioeconomic deprivation indices and scores of GP postgraduate training practices were compared against general practice in NI by examining the representation of practices whose patients live in areas of blanket deprivation, higher deprivation, and higher affluence. RESULTS: = 0.041. The proportion of training practices with blanket deprivation and higher levels of deprivation was underrepresented, with the current postgraduate GP training practices having more affluent populations. CONCLUSION: Postgraduate training practices had a statistically significant lower deprivation score and did not fully reflect the socioeconomic make-up of wider NI general practice. The results, however, are more favourable than in other areas of the UK and better than undergraduate teaching opportunities in general practice. Health inequalities will worsen if the representation of general practice training in areas of greater socioeconomic deprivation is not increased.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".