An opportunity to be grateful for? Exploring discourses about international medical graduates from India and Pakistan to the UK between 1960 and 1980
Bibliographic record
Abstract
INTRODUCTION: Following India and Pakistan gaining independence from British colonial rule, many doctors from these countries migrated to the UK and supported its fledgling National Health Service (NHS). Although this contribution is now widely celebrated, these doctors often faced hardship and hostility at the time and continue to face discrimination and racism in UK medical education. This study sought to examine discursive framings about Indian and Pakistani International Medical Graduates (IPIMGs) in the early period of their migration to the UK, between 1960 and 1980. METHODS: . We employed critical discourse analysis to examine knowledge and power relations in these texts, drawing on postcolonialism through the contrapuntal approach developed by Edward Said. RESULTS: The dominant discourse in this archive was one of opportunity. This included the opportunity for training, which was not available to IPIMGs in an equitable way, the missed opportunity to frame IPIMGs as saviours of the NHS rather than 'cheap labour', and the opportunity these doctors were framed to be held by being in the 'superior' British system, for which they should be grateful. Notably, there was also an opportunity to oppose, as IPIMGs challenged notions of incompetence directed at them. CONCLUSION: As IPIMGs in the UK continue to face discrimination, we shed light on how their cultural positioning has been historically founded and engrained in the imagination of the British medical profession by examining discursive trends to uncover historical tensions and contradictions.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.041 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| 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".